repo_name stringlengths 9 75 | topic stringclasses 30
values | issue_number int64 1 203k | title stringlengths 1 976 | body stringlengths 0 254k | state stringclasses 2
values | created_at stringlengths 20 20 | updated_at stringlengths 20 20 | url stringlengths 38 105 | labels listlengths 0 9 | user_login stringlengths 1 39 | comments_count int64 0 452 |
|---|---|---|---|---|---|---|---|---|---|---|---|
aminalaee/sqladmin | fastapi | 594 | Invalid time picker behaviour | ### Checklist
- [X] The bug is reproducible against the latest release or `master`.
- [x] There are no similar issues or pull requests to fix it yet.
### Describe the bug
The `TimeField` from wtforms accepts only time in format `"%H:%M"`. But UI requires to present time in another format, `"H:i:s"`. So, attempts to set time object field causes an error (UI imposes invalid format).
### Steps to reproduce the bug
```python
from datetime import time
from sqlalchemy import create_engine
from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column
import uvicorn
from fastapi import FastAPI
from sqladmin import Admin, ModelView
engine = create_engine('sqlite:///test.db')
class Base(DeclarativeBase):
pass
class Test(Base):
__tablename__ = 'test_t'
id: Mapped[int] = mapped_column(primary_key=True)
test_c: Mapped[time] = mapped_column()
class TestView(ModelView, model=Test):
column_list = [table.c.test]
Base.metadata.create_all(engine)
app = FastAPI()
admin = Admin(app, engine=engine)
admin.add_view(TestView)
uvicorn.run(app)
```
### Expected behavior
I can create new record within `test_t` table.
### Actual behavior
I can't.
### Debugging material
<img width="1217" alt="Снимок экрана 2023-08-29 в 02 21 30" src="https://github.com/aminalaee/sqladmin/assets/67328068/571bd66d-a37d-418c-b108-4fa5caaaf524">
### Environment
```txt
Python 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15)
[Clang 6.0 (clang-600.0.57)] on darwin
```
```txt
sqladmin==0.14.1
WTForms==3.0.1
SQLAlchemy==2.0.17
uvicorn==0.21.1
```
### Additional context
_No response_ | closed | 2023-08-28T23:36:32Z | 2023-08-30T17:15:45Z | https://github.com/aminalaee/sqladmin/issues/594 | [] | TheArcherST | 2 |
ageitgey/face_recognition | machine-learning | 789 | How to get the results of landmarks with 68 points as dlib | ### Description
How to get the results of landmarks with 68 points as dlib.
I don't wanna use `import dlib; etc.. ` . Cause it takes too much time.
When i was trying to get landmarks, i used `api.face_landmarks(image)`. But what i got is 72 points. And i tried to manually changed the results to 68 points according to the results used by dlib.
What i did was transforming 72 points to 68 points:
``` python
def compatibleToDlib(arr):
results = []
# 0 - 54
for i1 in range(55):
results.append(arr[i1])
# 55,56,57,58,59 <- arr[61-65]
for i2 in range(61,66):
results.append(arr[i2])
# 60 <- arr[59]
results.append(arr[59])
# 61,62,63 <- arr[58-56]
for i3 in range(0,3):
results.append(arr[-i3+58])
# 64 <- arr[55]
results.append(arr[55])
# 65,66,67 <- arr[70,69,68]
for i4 in range(0,3):
results.append(arr[-i4+70])
# print(len(results))
return results
```
But it didnt work tho. Cause the project that i needed to work with is `deepfakes` and tried the conversion part afterwards, but it says wrong aligened.
`shapes (2,51) and (55,2) not aligned: 51 (dim 1) != 55 (dim 0)` And i tried to replace the landmarks by the landmarks created by dlib. And it works.
### So what i want to know is how to get the same aligned results of 68-point landmarks as dlib by only using this repo - face_recognition ?
Thanks so much!!
| open | 2019-03-29T02:47:39Z | 2022-03-15T16:47:47Z | https://github.com/ageitgey/face_recognition/issues/789 | [] | zoeleesss | 1 |
miguelgrinberg/Flask-SocketIO | flask | 849 | Can't tell if its a client or server issue with simple app. | I'm having trouble with my on_...._response handlers. They don't seem to get an of the data being sent by the server. I think I have the server configure properly. It's based on the sample programs.
I'm sure I'm doing something wrong and was hoping you could help.
The code is here: https://gist.github.com/ilovetogetspamed/b4e013fd860a8d3cf753205747bb9cb6
I tried it two ways with similar results.
Thanks | closed | 2018-11-30T21:57:23Z | 2018-12-03T17:06:57Z | https://github.com/miguelgrinberg/Flask-SocketIO/issues/849 | [
"question"
] | ilovetogetspamed | 3 |
scikit-learn/scikit-learn | data-science | 30,832 | Numpy Array Error when Training MultioutputClassifer with LogisticRegressionCV with classes underrepresented | ### Describe the bug
When I train the MultioutputClassifer with LogisticRegressionCV with classes underrepresented, I get the following numpy error.
I think this is connected to the issue #28178 and #26401.
### Steps/Code to Reproduce
```python
import sklearn
print(sklearn.__version__)
from sklearn.linear_model import LogisticRegressionCV
from sklearn.multioutput import MultiOutputClassifier
import numpy as np
n, m = 20, 5
model = MultiOutputClassifier(LogisticRegressionCV())
X = np.random.randn(n, m)
y = np.concatenate([[np.random.randint(0, 2, n),
np.random.randint(0, 5, n)]], axis=0).T
y[-3:, 0] = [3, 4, 5]
model.fit(X, y)
```
### Expected Results
1.6.1
### Actual Results
1.6.1
```pytb
.venv/lib/python3.12/site-packages/sklearn/model_selection/_split.py:805: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.
warnings.warn(
Traceback (most recent call last):
File "error_skitlearn.py", line 14, in <module>
model.fit(X, y)
File ".venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 543, in fit
super().fit(X, Y, sample_weight=sample_weight, **fit_params)
File ".venv/lib/python3.12/site-packages/sklearn/base.py", line 1389, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 274, in fit
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.12/site-packages/sklearn/utils/parallel.py", line 77, in __call__
return super().__call__(iterable_with_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.12/site-packages/joblib/parallel.py", line 1918, in __call__
return output if self.return_generator else list(output)
^^^^^^^^^^^^
File ".venv/lib/python3.12/site-packages/joblib/parallel.py", line 1847, in _get_sequential_output
res = func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.12/site-packages/sklearn/utils/parallel.py", line 139, in __call__
return self.function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 63, in _fit_estimator
estimator.fit(X, y, **fit_params)
File ".venv/lib/python3.12/site-packages/sklearn/base.py", line 1389, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py", line 2038, in fit
coefs_paths = np.reshape(
^^^^^^^^^^^
File ".venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py", line 324, in reshape
return _wrapfunc(a, 'reshape', shape, order=order)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py", line 54, in _wrapfunc
return _wrapit(obj, method, *args, **kwds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py", line 42, in _wrapit
conv = _array_converter(obj)
^^^^^^^^^^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
```
### Versions
```shell
System:
python: 3.12.3 (main, Jan 17 2025, 18:03:48) [GCC 13.3.0]
executable: .venv/bin/python
machine: Linux-6.8.0-52-generic-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.6.1
pip: 25.0.1
setuptools: 75.8.0
numpy: 2.2.3
scipy: 1.15.1
Cython: None
pandas: 2.2.3
matplotlib: 3.10.0
joblib: 1.4.2
threadpoolctl: 3.5.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 8
prefix: libscipy_openblas
filepath: .venv/lib/python3.12/site-packages/numpy.libs/libscipy_openblas64_-6bb31eeb.so
version: 0.3.28
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 8
prefix: libscipy_openblas
filepath: .venv/lib/python3.12/site-packages/scipy.libs/libscipy_openblas-68440149.so
version: 0.3.28
threading_layer: pthreads
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 8
prefix: libgomp
filepath: .venv/lib/python3.12/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
version: None
``` | open | 2025-02-14T10:34:16Z | 2025-03-10T09:46:04Z | https://github.com/scikit-learn/scikit-learn/issues/30832 | [
"Bug"
] | lionelkusch | 4 |
developmentseed/lonboard | jupyter | 7 | Integrate with ipywidgets ColorPicker widget | https://ipywidgets.readthedocs.io/en/stable/reference/ipywidgets.html#ipywidgets.widgets.widget_color.ColorPicker
<img width="579" alt="image" src="https://github.com/developmentseed/lonboard/assets/15164633/4ab5c646-6820-474d-9596-78b2496f5a1f">
Just need to be able to parse the hex to RGB | closed | 2023-09-29T18:09:23Z | 2023-10-27T18:48:56Z | https://github.com/developmentseed/lonboard/issues/7 | [
"python"
] | kylebarron | 0 |
labmlai/annotated_deep_learning_paper_implementations | machine-learning | 101 | Switch Transformer with Mixture-of-Experts Ouput | If an ST is used for transfer-learning is the output a dense or sparse vector and of what size?
| closed | 2021-11-02T14:44:57Z | 2021-11-03T12:28:48Z | https://github.com/labmlai/annotated_deep_learning_paper_implementations/issues/101 | [
"question"
] | dineshbvadhia | 5 |
Gozargah/Marzban | api | 1,480 | notifications bot | درود اوقات به کام
چطور میتونم قبل از اتمام حجم یا تاریخ اعلان دریافت کنم
Customize all notifications
NOTIFY_STATUS_CHANGE = True
NOTIFY_USER_CREATED = false
NOTIFY_USER_UPDATED = false
NOTIFY_USER_DELETED = false
NOTIFY_USER_DATA_USED_RESET = false
NOTIFY_USER_SUB_REVOKED = True
NOTIFY_IF_DATA_USAGE_PERCENT_REACHED = True
NOTIFY_IF_DAYS_LEF_REACHED = True
NOTIFY_LOGIN = false
NOTIFY_DAYS_LEFT = 3
NOTIFY_REACHED_USAGE_PERCENT = 85
من از این موارد رو در فایل .env قرار دادم اما قبل از اتمام حجم و تاریخ ربات بهم اطلاع نمیده | closed | 2024-12-02T18:46:47Z | 2024-12-07T07:54:04Z | https://github.com/Gozargah/Marzban/issues/1480 | [] | mohsenzt | 2 |
litestar-org/polyfactory | pydantic | 41 | Feature: access already generated fields in custom generation | It would be a nice feature to have access to the already generated field values in a customised generator scenario, e.g.
```python
from typing import Any
from pydantic_factories import ModelFactory
class CustomFactory(ModelFactory[Any]):
"""Tweak the ModelFactory to add our custom mocks."""
@classmethod
def get_mock_value(cls, field_type: Any, previous_fields: dict[str, Any]) -> Any:
"""Add our custom mock value."""
if str(field_type) == "my_dependant_field" and previous_fields["my_relying_on"] == 'special_value':
return cls._get_faker().date_time_between()
return super().get_mock_value(field_type)
```
I could even imagine some decorator or annotation based solution to the same problem, e.g.
```python
class MyFactory(ModelFactory):
__model__ = MyModel
@depends('field_relying_on')
def dependant_field_name(self, current_value: Any, other_value: Any):
return 'special_value_generated_based_on_context'
``` | closed | 2022-05-05T14:43:03Z | 2022-05-27T16:13:02Z | https://github.com/litestar-org/polyfactory/issues/41 | [
"enhancement"
] | blagasz | 17 |
jschneier/django-storages | django | 628 | Adding handling of media files to the documentation | Would it be possible to add documentation to the handling of media files?
It would have been really helpful in my case:
From @elnygren 's post on stackoverflow: https://stackoverflow.com/questions/34247702/configure-django-and-google-cloud-storage
and answer to https://github.com/jschneier/django-storages/issues/491
I struggled a lot with handling media files between a django website on an appengine and static files in google cloud storage (both static markup files and media files). After a long try, I finally managed to succeed with the following settings.
Credit to @elnygren!
In my settings.py I include the following:
```
DEFAULT_FILE_STORAGE = 'config.storage_backends.GoogleCloudMediaStorage'
STATICFILES_STORAGE = 'config.storage_backends.GoogleCloudStaticStorage'
GS_PROJECT_ID = '<project-id>'
GS_MEDIA_BUCKET_NAME = '<media-bucket-name>'
GS_STATIC_BUCKET_NAME = '<static-bucket-name>'
STATIC_URL = 'https://storage.googleapis.com/{}/'.format(GS_STATIC_BUCKET_NAME)
MEDIA_URL = 'https://storage.googleapis.com/{}/'.format(GS_MEDIA_BUCKET_NAME)
GS_DEFAULT_ACL = 'private' # makes the files to private
```
Credit to @elnygren!
Creating a folder in the root directoy named 'config' I inserted the following 'storage_backends.py' file:
```
"""
GoogleCloudStorage extensions suitable for handing Django's
Static and Media files.
Requires following settings:
MEDIA_URL, GS_MEDIA_BUCKET_NAME
STATIC_URL, GS_STATIC_BUCKET_NAME
In addition to
https://django-storages.readthedocs.io/en/latest/backends/gcloud.html
"""
from django.conf import settings
from storages.backends.gcloud import GoogleCloudStorage
from storages.utils import setting
from urllib.parse import urljoin
class GoogleCloudMediaStorage(GoogleCloudStorage):
"""GoogleCloudStorage suitable for Django's Media files."""
def __init__(self, *args, **kwargs):
if not settings.MEDIA_URL:
raise Exception('MEDIA_URL has not been configured')
kwargs['bucket_name'] = setting('GS_MEDIA_BUCKET_NAME', strict=True)
super(GoogleCloudMediaStorage, self).__init__(*args, **kwargs)
def url(self, name):
""".url that doesn't call Google."""
return urljoin(settings.MEDIA_URL, name)
class GoogleCloudStaticStorage(GoogleCloudStorage):
"""GoogleCloudStorage suitable for Django's Static files"""
def __init__(self, *args, **kwargs):
if not settings.STATIC_URL:
raise Exception('STATIC_URL has not been configured')
```
App engine already instantiate GOOGLE_APPLICATION_CREDENTIALS so for development, I added:
`os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/path/to/service_account.json"`
in my "dev settings"
| closed | 2018-11-04T21:41:52Z | 2019-01-11T18:42:14Z | https://github.com/jschneier/django-storages/issues/628 | [] | kasperj93 | 8 |
MycroftAI/mycroft-core | nlp | 2,973 | Fail to install packages on arch | I had to replace `yes | sudo pacman -S ...` for `sudo pacman -S --noconfirm --needed`. Because it was failing to install the packages on arch linux.
| closed | 2021-08-11T00:30:36Z | 2021-08-21T17:58:23Z | https://github.com/MycroftAI/mycroft-core/issues/2973 | [] | ghost | 2 |
littlecodersh/ItChat | api | 87 | 加好友，能获取到信息，但无法自动同意 | > @itchat.msg_register(FRIENDS)
> def add_friend(msg):
> print msg
> itchat.add_friend(**msg['Text']) # 该操作会自动将新好友的消息录入，不需要重载通讯录
> itchat.send_msg('Nice to meet you!', msg['RecommendInfo']['UserName'])
能够打印出json的新的好友信息，但并不能自动同意。
| closed | 2016-09-26T16:02:06Z | 2016-09-27T03:47:04Z | https://github.com/littlecodersh/ItChat/issues/87 | [
"bug"
] | poach123 | 1 |
ivy-llc/ivy | pytorch | 28,608 | Fix Frontend Failing Test: tensorflow - non_linear_activations.jax.nn.softplus | closed | 2024-03-14T22:13:36Z | 2024-03-16T18:20:21Z | https://github.com/ivy-llc/ivy/issues/28608 | [
"Sub Task"
] | samthakur587 | 0 | |
langmanus/langmanus | automation | 98 | 一直报错, 不知道什么情况 | 能正常运行, 前端控制台一直输出报错信息, 不知道什么情况
终端中没有看见报错
# 控制台输出如下(代理模型请求都是200, 信息已删除)
2025-03-21 17:19:02,647 - src.service.workflow_service - INFO - Starting workflow with user input: [{'role': 'user', 'content': '我是做私域方面ai培训的, 请提供话术与直播脚本,文案,素材等, 供我直播时吸引别人观'}]
2025-03-21 17:19:02,654 - src.graph.nodes - INFO - Coordinator talking.
2025-03-21 17:19:05,672 - src.graph.nodes - INFO - Planner generating full plan
2025-03-21 17:19:53,267 - src.graph.nodes - INFO - Supervisor evaluating next action
2025-03-21 17:19:55,500 - src.graph.nodes - INFO - Supervisor delegating to: researcher
2025-03-21 17:19:55,504 - src.graph.nodes - INFO - Research agent starting task
Warning: node executable not found, reverting to pure-Python mode. Install Node.js v10 or newer to use Readability.js.
Warning: node executable not found, reverting to pure-Python mode. Install Node.js v10 or newer to use Readability.js.
Warning: node executable not found, reverting to pure-Python mode. Install Node.js v10 or newer to use Readability.js.
Warning: node executable not found, reverting to pure-Python mode. Install Node.js v10 or newer to use Readability.js.
Warning: node executable not found, reverting to pure-Python mode. Install Node.js v10 or newer to use Readability.js.
Warning: node executable not found, reverting to pure-Python mode. Install Node.js v10 or newer to use Readability.js.
Warning: node executable not found, reverting to pure-Python mode. Install Node.js v10 or newer to use Readability.js.
Warning: node executable not found, reverting to pure-Python mode. Install Node.js v10 or newer to use Readability.js.
2025-03-21 17:20:15,625 - src.graph.nodes - INFO - Research agent completed task
2025-03-21 17:20:15,629 - src.graph.nodes - INFO - Supervisor evaluating next action
2025-03-21 17:20:16,641 - src.graph.nodes - INFO - Supervisor delegating to: reporter
2025-03-21 17:20:16,644 - src.graph.nodes - INFO - Reporter write final report
2025-03-21 17:20:23,258 - src.graph.nodes - INFO - Supervisor evaluating next action
2025-03-21 17:20:24,410 - src.graph.nodes - INFO - Workflow completed
# 下发issue提醒信息如下
## 报错信息1
Error: Maximum update depth exceeded. This can happen when a component calls setState inside useEffect, but useEffect either doesn't have a dependency array, or one of the dependencies changes on every render.
at createUnhandledError (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:879:71)
at handleClientError (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:1052:56)
at console.error (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:1191:56)
at getRootForUpdatedFiber (http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:4702:143)
at enqueueConcurrentRenderForLane (http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:4689:16)
at forceStoreRerender (http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:5854:20)
at http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:5840:45
at http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:12095:39
at Array.forEach (<anonymous>)
at _devbuildindicator.devBuildIndicator.hide (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:12095:19)
at handleDevBuildIndicatorHmrEvents (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:14491:54)
at WebSocket.handler (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:15040:88)
## 报错信息2
Error: parsed json with extra tokens: {}
at createUnhandledError (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:879:71)
at handleClientError (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:1052:56)
at console.error (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:1191:56)
at push.[project]/node_modules/.pnpm/best-effort-json-parser@1.1.3/node_modules/best-effort-json-parser/dist/parse.js [app-client] (ecmascript).parse.onExtraToken (http://localhost:3000/_next/static/chunks/node_modules__pnpm_b85362bd._.js:16263:17)
at parse (http://localhost:3000/_next/static/chunks/node_modules__pnpm_b85362bd._.js:16254:23)
at PlanTaskView.useMemo[plan] (http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:2000:283)
at updateMemo (http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:6225:21)
at Object.useMemo (http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:15198:24)
at exports.useMemo (http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:1672:36)
at PlanTaskView (http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:1989:318)
at http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:1899:556
at Array.map (<anonymous>)
at http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:1899:156
at Array.map (<anonymous>)
at WorkflowProgressView (http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:1880:45)
at MessageView (http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:2211:345)
at http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:2153:366
at Array.map (<anonymous>)
at MessageHistoryView (http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:2153:22)
at HomePage (http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:2301:360)
at ClientPageRoot (http://localhost:3000/_next/static/chunks/2756c_next_dist_f0270881._.js:2053:50)
## 报错3
Error: parsed json with extra tokens: {}
at createUnhandledError (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:879:71)
at handleClientError (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:1052:56)
at console.error (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:1191:56)
at push.[project]/node_modules/.pnpm/best-effort-json-parser@1.1.3/node_modules/best-effort-json-parser/dist/parse.js [app-client] (ecmascript).parse.onExtraToken (http://localhost:3000/_next/static/chunks/node_modules__pnpm_b85362bd._.js:16263:17)
at parse (http://localhost:3000/_next/static/chunks/node_modules__pnpm_b85362bd._.js:16254:23)
at PlanTaskView.useMemo[plan] (http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:2000:283)
at updateMemo (http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:6229:17)
at Object.useMemo (http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:15198:24)
at exports.useMemo (http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:1672:36)
at PlanTaskView (http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:1989:318)
at http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:1899:556
at Array.map (<anonymous>)
at http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:1899:156
at Array.map (<anonymous>)
at WorkflowProgressView (http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:1880:45)
at MessageView (http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:2211:345)
at http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:2153:366
at Array.map (<anonymous>)
at MessageHistoryView (http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:2153:22)
at HomePage (http://localhost:3000/_next/static/chunks/src_a58de9b6._.js:2301:360)
at ClientPageRoot (http://localhost:3000/_next/static/chunks/2756c_next_dist_f0270881._.js:2053:50)
## 报错4
Error: Maximum update depth exceeded. This can happen when a component calls setState inside useEffect, but useEffect either doesn't have a dependency array, or one of the dependencies changes on every render.
at createUnhandledError (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:879:71)
at handleClientError (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:1052:56)
at console.error (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:1191:56)
at getRootForUpdatedFiber (http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:4702:143)
at enqueueConcurrentRenderForLane (http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:4689:16)
at forceStoreRerender (http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:5854:20)
at http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:5840:45
at http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:12091:39
at Array.forEach (<anonymous>)
at _devbuildindicator.devBuildIndicator.show (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:12091:19)
at handleDevBuildIndicatorHmrEvents (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:14487:54)
at WebSocket.handler (http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:15040:88)
## 报错5
Error: Maximum update depth exceeded. This can happen when a component repeatedly calls setState inside componentWillUpdate or componentDidUpdate. React limits the number of nested updates to prevent infinite loops.
at getRootForUpdatedFiber (http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:4701:171)
at enqueueConcurrentHookUpdate (http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:4685:16)
at dispatchSetStateInternal (http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:6448:22)
at dispatchSetState (http://localhost:3000/_next/static/chunks/2756c_next_dist_compiled_0fbf8836._.js:6421:9)
at http://localhost:3000/_next/static/chunks/2756c_next_dist_client_198cdfc1._.js:13911:17 | open | 2025-03-21T09:32:04Z | 2025-03-21T09:32:04Z | https://github.com/langmanus/langmanus/issues/98 | [
"bug"
] | Amon1412 | 0 |
ultralytics/ultralytics | python | 18,727 | Performance Drop When Using OBB | ### Search before asking
- [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/ultralytics/ultralytics/discussions) and found no similar questions.
### Question
I have compared the performance of OBB and HBB for object detection using YOLOv8. Why do I find out that the performance of my implementation of OBB is significantly worse than HBB (both precision and recall)? The precision is around 0.57 and recall around 0.6 for OBB while precision and recall are around 0.75 for HBB. OBB should in principle have better performance than HBB, isn't it?
### Additional
_No response_ | open | 2025-01-17T06:48:53Z | 2025-01-17T07:30:32Z | https://github.com/ultralytics/ultralytics/issues/18727 | [
"question",
"OBB"
] | KHC1234 | 10 |
pytorch/pytorch | numpy | 149,718 | Constraints for distributions with mixed support | ### 🚀 The feature, motivation and pitch
I'd like to implement a joint distribution of both discrete and continuous parameters and would like to be able to define a constraint that indicates that the support is mixed and which parameters are continuous. A potential use-case is representing an approximate posterior distribution (with continuous _and_discrete parameters) with a distribution to use it as a prior when new data comes in in a Pyro model.
`Distributions.support` is required to be a child of `Constraint`, and `Constraint.is_discrete` returns a `bool`. `constraints._Cat`, for example, returns `True` for `is_discrete` if _any_ of the component constraints are discrete. i.e. PyTorch treats distributions with discrete support as having a support that is entirely discrete.
I can think of 2 solutions:
1. Allow `Constraint.is_discrete` to return a `Tensor` of `Bool` indicating which parameters would have which constraints.
2. Allow `Distribution.support` to return a `Tensor` of `Constraint` indicating which parameters would have which constraints.
### Alternatives
_No response_
### Additional context
_No response_
cc @fritzo @neerajprad @alicanb @nikitaved | open | 2025-03-21T09:03:28Z | 2025-03-21T15:07:28Z | https://github.com/pytorch/pytorch/issues/149718 | [
"module: distributions",
"triaged"
] | sethaxen | 0 |
ansible/awx | django | 15,486 | Error when installing AWX using playbook | ### Please confirm the following
- [X] I agree to follow this project's [code of conduct](https://docs.ansible.com/ansible/latest/community/code_of_conduct.html).
- [X] I have checked the [current issues](https://github.com/ansible/awx/issues) for duplicates.
- [X] I understand that AWX is open source software provided for free and that I might not receive a timely response.
- [X] I am **NOT** reporting a (potential) security vulnerability. (These should be emailed to `security@ansible.com` instead.)
### Bug Summary
I'm trying to work Final year project using Ansible AWX. But getting error when installing AWX using playbook
### AWX version
Ansible(core 2.17.30)
### Select the relevant components
- [ ] UI
- [ ] UI (tech preview)
- [ ] API
- [X] Docs
- [ ] Collection
- [ ] CLI
- [X] Other
### Installation method
docker development environment
### Modifications
no
### Ansible version
Ansible(core 2.17.30)
### Operating system
Ubuntu 22.04
### Web browser
Chrome, Edge
### Steps to reproduce
I'm getting below error when running the play book 'ansible-playbook -i inventory install.yml
Already python3 is installed on AWS ubuntu 22.04.
I used the steps to install from this website https://medium.com/@sahildrive007/easily-install-ansible-awx-on-ubuntu-a-step-by-step-guide-b391c783d54c
### Expected results
I thought it will run but didn't. Not sure how to set the interpreter. If you know please explain in detail. I'm new to work ubuntu.
### Actual results
Getting below error when running playbook.
[DEPRECATION WARNING]: community.docker.docker_compose has been deprecated. This module uses docker-compose v1, which is End of life since july 2022. Please migrate to community.docker.docker_compose_v2. this feature will be removed from community.docker in version 4.0.0. Deprecation warnings can be disabled by setting deprecation_warnings=False in ansible.cfg.
PLAY [Build and deploy AWX] ************************************************************
TASK [Gathering Facts] *****************************************************************
fatal: [localhost]: fAILED! => {'ansible_facts":{}, "changed": false, "failed_modules": {"ansible.legacy.setup": {"failed": true, "module_stderr": "/bin/sh: 1: /usr/bin/env python3: not found\n", "module_stdout": "", "msg": "The module failed to execute correctly, you probably need to set the interpreter.\nSee stdout/stderr for the exact error", "rc": 127}}, "msg": "the following module failed to execute: ansible.legacy.setup\n"}
PLAY RECAP **************************************************************************
### Additional information
_No response_ | closed | 2024-09-03T18:55:54Z | 2024-09-04T19:03:01Z | https://github.com/ansible/awx/issues/15486 | [
"type:bug",
"component:docs",
"needs_triage",
"community"
] | Manimala94 | 1 |
huggingface/transformers | python | 36,096 | flex_attention does not output the full attention_weights with output_attention option | ### System Info
transformers==4.45.1
torch==2.6.0
### Who can help?
_No response_
### Information
- [x] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)
### Reproduction
```attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
logger.warning_once(
"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
else:
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
### Expected behavior
The output_attention option (e.g. in [llama](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L232)) is supposed to return the full attention weights of shape (bsize, nheads, len_q, len_k). Currently, transformers support this option with eager attention and flex_attention, but it looks like what [flex_attention](https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/flex_attention.py) returns are just the logsumexp scores of shape (bsize, nheads, len_q). It seems they do not support outputting the full attention weight either. | closed | 2025-02-07T19:24:12Z | 2025-03-18T08:04:25Z | https://github.com/huggingface/transformers/issues/36096 | [
"bug"
] | gblackout | 5 |
onnx/onnx | machine-learning | 6,422 | The opposite | https://github.com/onnx/onnx/blob/56cf02beb2331a75f2a119f8b6d64e9d460d3a98/onnx/onnx-ml.proto3#L320
I think it is not the gradient of "x" w.r.t. a chosen loss but the gradient of a chosen loss w.r.t. "x" | open | 2024-10-03T13:58:19Z | 2024-10-03T15:00:14Z | https://github.com/onnx/onnx/issues/6422 | [] | JeanLoupFarges | 1 |
jupyter-book/jupyter-book | jupyter | 1,328 | Embed a hyperlink in the sidebar logo | I saw some feature requests about this (especially #79), but it seems like there is no working workaround for this? A simple option like `link : www.myhomepage.tz` adding to the `_config.yml` file would be nice. Thus, when a user clicks on the logo he will be redirected (or a new tab is opened) to a corresponding website. | open | 2021-05-12T17:46:44Z | 2021-05-13T21:40:45Z | https://github.com/jupyter-book/jupyter-book/issues/1328 | [
":label: sphinx-book-theme"
] | hackenjoe | 1 |
coleifer/sqlite-web | flask | 106 | Edit Table Data? | Great project!
Is it possible to ask for the ability to directly EDIT table data? I.e. change data in a table and ADD or DELETE a row to a table?
Thanks for your efforts and consideration! | closed | 2022-08-12T05:00:45Z | 2024-04-09T07:17:57Z | https://github.com/coleifer/sqlite-web/issues/106 | [] | randyg3000 | 2 |
automl/auto-sklearn | scikit-learn | 1,741 | Install error on ubuntu / window - pip and conda |
I meet issue install this library on both window and ubuntu 24.04, and this is ubuntu, plz help! I try other library like TPOT, even successful but still cannot import.
with pip: error:
```
File "<string>", line 293, in setup_package
ModuleNotFoundError: No module named 'numpy.distutils'
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed
× Encountered error while generating package metadata.
╰─> See above for output.
note: This is an issue with the package mentioned above, not pip.
hint: See above for details.
```
with conda: error:
```
conda install conda-forge::auto-sklearn
Channels:
- defaults
- conda-forge
Platform: linux-64
Collecting package metadata (repodata.json): done
Solving environment: / warning libmamba Added empty dependency for problem type SOLVER_RULE_UPDATE
failed
LibMambaUnsatisfiableError: Encountered problems while solving:
- package auto-sklearn-0.12.5-pyhd8ed1ab_0 requires pyrfr >=0.8.1,<0.9, but none of the providers can be installed
Could not solve for environment specs
The following packages are incompatible
├─ auto-sklearn is installable and it requires
│ └─ pyrfr >=0.8.1,<0.9 with the potential options
│ ├─ pyrfr [0.8.1|0.8.2] would require
│ │ └─ python >=3.6,<3.7.0a0 , which can be installed;
│ ├─ pyrfr [0.8.1|0.8.2|0.8.3] would require
│ │ └─ python >=3.7,<3.8.0a0 , which can be installed;
│ ├─ pyrfr [0.8.1|0.8.2|0.8.3] would require
│ │ └─ python >=3.8,<3.9.0a0 , which can be installed;
│ ├─ pyrfr [0.8.1|0.8.2|0.8.3] would require
│ │ └─ python >=3.9,<3.10.0a0 , which can be installed;
│ ├─ pyrfr [0.8.2|0.8.3] would require
│ │ └─ python >=3.10,<3.11.0a0 , which can be installed;
│ └─ pyrfr 0.8.3 would require
│ └─ python >=3.11,<3.12.0a0 , which can be installed;
└─ pin-1 is not installable because it requires
└─ python 3.12.* , which conflicts with any installable versions previously reported.
```
| open | 2024-09-23T11:03:59Z | 2025-01-30T13:16:26Z | https://github.com/automl/auto-sklearn/issues/1741 | [] | ladylazy9x | 9 |
google-research/bert | nlp | 515 | Is there a larger unreleased BERT model? | GPT-2 has a 1.5 billion parameter model that is unreleased, probably to prevent competition now that OpenAI has decided to pursue profitability.
Is there a larger BERT model that is unreleased for similar reasons? | open | 2019-03-22T18:37:19Z | 2019-03-22T18:37:19Z | https://github.com/google-research/bert/issues/515 | [] | abhishmitra | 0 |
tensorpack/tensorpack | tensorflow | 1,032 | What does @layer_register(log_shape=True) do before a layer? | As the title mentioned, I'm curious about what @layer_register(log_shape=True) do before a layer?
such as
`@layer_register(log_shape=True)`
`def Conv2D(x, out_channel, kernel_shape,
padding='SAME', stride=1,
W_init=None, b_init=None,
nl=tf.nn.relu, split=1, use_bias=True):`
| closed | 2019-01-05T21:01:50Z | 2019-01-11T16:53:47Z | https://github.com/tensorpack/tensorpack/issues/1032 | [
"usage"
] | john81923 | 1 |
browser-use/browser-use | python | 211 | please merge os x chrome profile fix from web-ui into main repo | in order for os x users to have `browser-use` access their chrome profile, a workaround was needed.
this now works in the `web-ui` repo. please merge this into the main project so we can use without the web-ui.
https://github.com/browser-use/web-ui/pull/83 | closed | 2025-01-11T16:51:34Z | 2025-01-13T13:09:05Z | https://github.com/browser-use/browser-use/issues/211 | [] | rawwerks | 2 |
Gozargah/Marzban | api | 1,029 | نحوه فعال سازی فرگمنت برای یک اینباند | درود
چطوری میشه فقط برای یک اینباند خاص فرگمنت رو فعال کرد نه همه کانفیگ ها؟ | closed | 2024-06-03T12:41:46Z | 2024-07-15T20:54:13Z | https://github.com/Gozargah/Marzban/issues/1029 | [] | rbsdotnet | 0 |
mkhorasani/Streamlit-Authenticator | streamlit | 87 | Azure AAD Oauth2 Support | Hello,
I want to add user authorization system to the application with Azure AD. How can I do that. Does this module have such support? I would appreciate your help. | closed | 2023-09-08T12:41:28Z | 2024-09-30T06:49:27Z | https://github.com/mkhorasani/Streamlit-Authenticator/issues/87 | [
"enhancement"
] | cnhsn | 4 |
SYSTRAN/faster-whisper | deep-learning | 1,176 | Poor performance for Vietnamese diarization | closed | 2024-11-27T10:25:12Z | 2024-11-27T10:25:30Z | https://github.com/SYSTRAN/faster-whisper/issues/1176 | [] | dohuyduc2002 | 0 | |
globaleaks/globaleaks-whistleblowing-software | sqlalchemy | 3,038 | Getting error email multiple times : Error Message: TypeError: Cannot read property 'split' of undefined | Hello,
I am getting 4 emails at a time stating " Error Message: TypeError: Cannot read property 'split' of undefined ".
I've started getting this after the update from v4.2.12 to v4.2.13.
Note: I've done an apt-update and apt-upgrade on ubuntu. And i am using version 20 LTS.
Any idea why this error message?
Version: 4.2.13
URL: /status/xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx
User Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.159 Safari/537.36
Error Message: TypeError: Cannot read property 'split' of undefined
Stacktrace:
[
{
"columnNumber": 240267,
"lineNumber": 64,
"fileName": "http://complaints.icac.mu/js/scripts.min.js",
"source": " at http://complaints.icac.mu/js/scripts.min.js:64:240267"
},
{
"columnNumber": 114346,
"lineNumber": 8,
"fileName": "http://complaints.icac.mu/js/scripts.min.js",
"functionName": "Array.<anonymous>",
"source": " at Array.<anonymous> (http://complaints.icac.mu/js/scripts.min.js:8:114346)"
},
{
"columnNumber": 114327,
"lineNumber": 8,
"fileName": "http://complaints.icac.mu/js/scripts.min.js",
"source": " at http://complaints.icac.mu/js/scripts.min.js:8:114327"
},
{
"columnNumber": 93072,
"lineNumber": 8,
"fileName": "http://complaints.icac.mu/js/scripts.min.js",
"source": " at http://complaints.icac.mu/js/scripts.min.js:8:93072"
},
{
"columnNumber": 101303,
"lineNumber": 8,
"fileName": "http://complaints.icac.mu/js/scripts.min.js",
"functionName": "d.$digest",
"source": " at d.$digest (http://complaints.icac.mu/js/scripts.min.js:8:101303)"
},
{
"columnNumber": 103100,
"lineNumber": 8,
"fileName": "http://complaints.icac.mu/js/scripts.min.js",
"functionName": "d.$apply",
"source": " at d.$apply (http://complaints.icac.mu/js/scripts.min.js:8:103100)"
},
{
"columnNumber": 66702,
"lineNumber": 8,
"fileName": "http://complaints.icac.mu/js/scripts.min.js",
"functionName": "_",
"source": " at _ (http://complaints.icac.mu/js/scripts.min.js:8:66702)"
},
{
"columnNumber": 68927,
"lineNumber": 8,
"fileName": "http://complaints.icac.mu/js/scripts.min.js",
"functionName": "C",
"source": " at C (http://complaints.icac.mu/js/scripts.min.js:8:68927)"
},
{
"columnNumber": 68274,
"lineNumber": 8,
"fileName": "http://complaints.icac.mu/js/scripts.min.js",
"functionName": "XMLHttpRequest.b.onload",
"source": " at XMLHttpRequest.b.onload (http://complaints.icac.mu/js/scripts.min.js:8:68274)"
}
] | open | 2021-08-30T05:01:52Z | 2021-08-31T10:52:36Z | https://github.com/globaleaks/globaleaks-whistleblowing-software/issues/3038 | [] | divium-singh | 5 |
keras-team/keras | pytorch | 20,955 | Error with variable.regularizer in keras.layers.TFSMLayer | I created a universal sentence encoder layer as an instance of `keras.layers.TFSMLayer` and used it in a binary classifier. Training the model results in the following error:
```
Epoch 1/10
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
[<ipython-input-7-d7863a64cd24>](https://localhost:8080/#) in <cell line: 0>()
2 y_train = keras.ops.array([[1], [0], [0]])
3
----> 4 nn_model_history = nn_model.fit(
5 x = x_train,
6 y = y_train,
1 frames
[/usr/local/lib/python3.11/dist-packages/keras/src/layers/layer.py](https://localhost:8080/#) in _get_regularization_losses(self)
1152 weight_regularization_losses = []
1153 for variable in self.trainable_weights:
-> 1154 if variable.regularizer is None:
1155 continue
1156 if backend.in_stateless_scope() and not in_symbolic_scope():
AttributeError: 'UninitializedVariable' object has no attribute 'regularizer'
```
The [Keras layers code](https://github.com/keras-team/keras/blob/master/keras/src/layers/layer.py) fails when the trainable weights lack a `regularizer` attribute.
I've reproduced the error in [this Colab notebook](https://colab.research.google.com/drive/1F-RsSZNrr0bH4EUwkjkm0LE8XyVg6IMB?usp=sharing). | closed | 2025-02-24T21:04:34Z | 2025-03-08T18:22:39Z | https://github.com/keras-team/keras/issues/20955 | [
"type:Bug"
] | rlcauvin | 9 |
strawberry-graphql/strawberry | fastapi | 3,347 | Different contexts getters depending on the query or mutation | ## Feature Request Type
- [ ] Core functionality
- [X] Alteration (enhancement/optimization) of existing feature(s)
- [ ] New behavior
## Description
Currently the only way of doing context getters with fastapi integration is through multiple routers but then I would need to have different paths which I think would be weird in the graphql world. So I would like to be able of changing the context based on the resolver instead of setting only in the main router. | open | 2024-01-18T17:30:58Z | 2025-03-20T15:56:34Z | https://github.com/strawberry-graphql/strawberry/issues/3347 | [] | Focadecombate | 2 |
pydantic/pydantic-core | pydantic | 1,071 | `pydantic_core-2.14.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl` is missing. | In version 2.10.1, this wheel exists, but it is missing in latest release(2.14.1).
See https://pypi.org/project/pydantic-core/2.14.1/#files
See https://pypi.org/project/pydantic-core/2.10.1/#files
P.S. Not only this one wheel is missing, but also some others are missing. | closed | 2023-11-14T05:15:27Z | 2023-11-14T14:12:22Z | https://github.com/pydantic/pydantic-core/issues/1071 | [] | overcat | 1 |
streamlit/streamlit | streamlit | 10,419 | Add support for more currency formats to `column_config.NumberColumn` | ### Checklist
- [x] I have searched the [existing issues](https://github.com/streamlit/streamlit/issues) for similar feature requests.
- [x] I added a descriptive title and summary to this issue.
### Summary
In https://github.com/streamlit/streamlit/pull/10179 we added support for a couple of pre-defined formats. This also includes `dollar` and `euro` to format values as currencies. Should we add more formats to cover other currencies as well? Maybe we can just support the full list of currencies supported by the [Intl.NumberFormat API](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Intl/NumberFormat/NumberFormat#currency_2).
### Why?
_No response_
### How?
_No response_
### Additional Context
_No response_ | open | 2025-02-17T17:11:07Z | 2025-02-17T17:11:21Z | https://github.com/streamlit/streamlit/issues/10419 | [
"type:enhancement",
"feature:st.column_config"
] | lukasmasuch | 1 |
aminalaee/sqladmin | asyncio | 218 | `sourceMappingURL` triggers access to non-existent file | ### Checklist
- [X] The bug is reproducible against the latest release or `master`.
- [X] There are no similar issues or pull requests to fix it yet.
### Describe the bug
The .js files specify `sourceMappingURL` but the `.map` files are not included in the package. So the browser tries to fetch it and gets a 404 error. We should add the source map files or remove the following lines:
https://github.com/aminalaee/sqladmin/blob/daea8c9c385794605ddeb02b7105d71ca9a9823f/sqladmin/statics/js/bootstrap.min.js#L7
### Steps to reproduce the bug
_No response_
### Expected behavior
_No response_
### Actual behavior
_No response_
### Debugging material
```
INFO: 127.0.0.1:8000 - "GET /js/bootstrap.min.js.map HTTP/1.1" 404 Not Found
```
### Environment
- Ubuntu 20.04.4 LTS
- Python 3.9.12
- SQLAdmin 0.1.11
### Additional context
_No response_ | closed | 2022-06-27T04:30:26Z | 2022-07-08T07:23:01Z | https://github.com/aminalaee/sqladmin/issues/218 | [
"good first issue"
] | okapies | 2 |
Gozargah/Marzban | api | 991 | وارپ با استفاده از هسته در Core SettingXray | سلام و احترام
توی وارپ با استفاده از هسته در Core SettingXray
{
"tag": "warp",
"protocol": "wireguard",
"settings": {
"secretKey": "Your_Secret_Key",
"DNS": "1.1.1.1",
"address": ["172.16.0.2/32", "2606:4700:110:8756:9135:af04:3778:40d9/128"],
"peers": [
{
"publicKey": "bmXOC+F1FxEMF9dyiK2H5/1SUtzH0JuVo51h2wPfgyo=",
"endpoint": "engage.cloudflareclient.com:2408"
}
],
"kernelMode": false
}
}
مواردی که در فایل wgcf-profile.conf قرار داره
access_token device_id license_key private_key
هر کدام از آیتمهای بالا را در کدام قسمت Core SettingXray قرار دهیم | closed | 2024-05-18T20:37:05Z | 2024-05-21T05:28:51Z | https://github.com/Gozargah/Marzban/issues/991 | [
"Duplicate",
"Invalid"
] | yaramahmadi | 2 |
plotly/plotly.py | plotly | 4,791 | make package versions explicit in packages/python/plotly/optional-requirements.txt | As the title says, `packages/python/plotly/optional-requirements.txt` should specify versions or version ranges for pandas, scipy, etc. | open | 2024-10-09T13:06:20Z | 2024-10-09T13:06:21Z | https://github.com/plotly/plotly.py/issues/4791 | [
"feature",
"P3"
] | gvwilson | 0 |
wkentaro/labelme | deep-learning | 499 | labelme2voc.py file wrongly puts annotations on the image | Hi, after creating .json files of segmentation annotations, I tried labelme2voc.py file to save original images, segmentation masks, and visualization of segmentation annotations on the original images. But, for some reason, annotation masks, and hence the visualization are not matching with the actual objects in the original image.
e.g. This is the original image.
And this is the image for annotation masks
The objects that I put polygon on were the 3 read blobs on the top side of the original image but somehow they appear on the left, as shown below.
Any idea, what might be the reason? I am working on Ubuntu. | closed | 2019-10-19T02:03:32Z | 2019-10-20T02:07:03Z | https://github.com/wkentaro/labelme/issues/499 | [] | sangramkapre | 1 |
microsoft/Bringing-Old-Photos-Back-to-Life | pytorch | 225 | module 'models.networks' has no attribute 'modify_commandline_options' | Running Stage 3: Face Enhancement
Traceback (most recent call last):
File "test_face.py", line 15, in <module>
opt = TestOptions().parse()
File "E:\picture_fix\Bringing-Old-Photos-Back-to-Life\Face_Enhancement\options\base_options.py", line 262, in parse
opt = self.gather_options()
File "E:\picture_fix\Bringing-Old-Photos-Back-to-Life\Face_Enhancement\options\base_options.py", line 197, in gather_options
parser = model_option_setter(parser, self.isTrain)
File "E:\picture_fix\Bringing-Old-Photos-Back-to-Life\Face_Enhancement\models\pix2pix_model.py", line 12, in modify_commandline_options
networks.modify_commandline_options(parser, is_train)
AttributeError: module 'models.networks' has no attribute 'modify_commandline_options'
Finish Stage 3 ... | open | 2022-03-17T01:41:33Z | 2022-04-26T00:23:55Z | https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life/issues/225 | [] | bill1969 | 2 |
CorentinJ/Real-Time-Voice-Cloning | python | 1,267 | Kılıcdaroğlu | Kzkgzpux | open | 2023-10-25T11:50:31Z | 2023-10-25T11:50:58Z | https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/1267 | [] | nejatsamedov | 0 |
joeyespo/grip | flask | 27 | shutdown grip server | Is it possible to shutdown the grip server in the CLI? If not, would it be hard to add this feature?
| closed | 2013-09-09T20:00:20Z | 2013-09-10T19:22:17Z | https://github.com/joeyespo/grip/issues/27 | [
"question"
] | kindrowboat | 4 |
lepture/authlib | flask | 235 | Support for EdDSA Algorithm | It looks like this is one of the new algorithms, and is listed on jwt.io. It's the only thing on there that you don't support. Keep it up! | closed | 2020-05-23T20:01:00Z | 2020-05-24T07:10:36Z | https://github.com/lepture/authlib/issues/235 | [
"spec",
"feature request"
] | benwis | 2 |
twopirllc/pandas-ta | pandas | 503 | Ray/Trendline/Linear Regression of two points | Say I have two points on the Dataframe (price and UNIX timestamp) and I want to draw a Ray (endless) that goes through these two points. Does Pandas TA or Pandas has a function for this? Is there an easy way to draw a Linear Regression ray, I just have the two points and I want to draw a Ray through them and get all the Datapoints (price and timestamp of each) say 1 week into the future to be predicted. Any example of how to use "linear_regression" ? | closed | 2022-03-15T18:34:43Z | 2022-05-02T22:12:39Z | https://github.com/twopirllc/pandas-ta/issues/503 | [
"help wanted",
"good first issue",
"info"
] | hadialaddin | 3 |
huggingface/datasets | pandas | 6,778 | Dataset.to_csv() missing commas in columns with lists | ### Describe the bug
The `to_csv()` method does not output commas in lists. So when the Dataset is loaded back in the data structure of the column with a list is not correct.
Here's an example:
Obviously, it's not as trivial as inserting commas in the list, since its a comma-separated file. But hopefully there's a way to export the list in a way that it'll be imported by `load_dataset()` correctly.
### Steps to reproduce the bug
Here's some code to reproduce the bug:
```python
from datasets import Dataset
ds = Dataset.from_dict(
{
"pokemon": ["bulbasaur", "squirtle"],
"type": ["grass", "water"]
}
)
def ascii_to_hex(text):
return [ord(c) for c in text]
ds = ds.map(lambda x: {"int": ascii_to_hex(x['pokemon'])})
ds.to_csv('../output/temp.csv')
```
temp.csv then contains:
```
### Expected behavior
ACTUAL OUTPUT:
```
pokemon,type,int
bulbasaur,grass,[ 98 117 108 98 97 115 97 117 114]
squirtle,water,[115 113 117 105 114 116 108 101]
```
EXPECTED OUTPUT:
```
pokemon,type,int
bulbasaur,grass,[98, 117, 108, 98, 97, 115, 97, 117, 114]
squirtle,water,[115, 113, 117, 105, 114, 116, 108, 101]
```
or probably something more like this since it's a CSV file:
```
pokemon,type,int
bulbasaur,grass,"[98, 117, 108, 98, 97, 115, 97, 117, 114]"
squirtle,water,"[115, 113, 117, 105, 114, 116, 108, 101]"
```
### Environment info
### Package Version
Name: datasets
Version: 2.16.1
### Python
version: 3.10.12
### OS Info
PRETTY_NAME="Ubuntu 22.04.4 LTS"
NAME="Ubuntu"
VERSION_ID="22.04"
VERSION="22.04.4 LTS (Jammy Jellyfish)"
VERSION_CODENAME=jammy
ID=ubuntu
ID_LIKE=debian
...
UBUNTU_CODENAME=jammy | open | 2024-04-04T16:46:13Z | 2024-04-08T15:24:41Z | https://github.com/huggingface/datasets/issues/6778 | [] | mpickard-dataprof | 1 |
KevinMusgrave/pytorch-metric-learning | computer-vision | 647 | Can MatchFinder implement the query library? | Hello! Thank you for developing this great library. It helps me try metric learning a lot.
I am conducting a binary classification task. I want to use feature vectors for similarity calculation to assist in the final test results.
I want to create a feature vector query library. My model has already obtained a set of labeled feature vectors, and I want to use MatchFinder to select the most representative feature vectors as the query library. Each time a test feature vector is obtained, it will be queried against the query library to determine the classification.
I am unsure how to implement this. Could you provide a simple example, or explain how MatchFinder can be used in this way? As a beginner, I hope you don't mind if I have any questions. | closed | 2023-07-10T09:22:40Z | 2023-07-24T16:01:27Z | https://github.com/KevinMusgrave/pytorch-metric-learning/issues/647 | [
"question"
] | ZH-UCAS | 1 |
MilesCranmer/PySR | scikit-learn | 339 | [Feature]: Add complexity calculation for user defined expression | ### Feature Request
Hi. I've recently started using PySR and I would like to suggest a new feature that I think would make the code even more user-friendly.
Would it be possible to have more direct access to the function that computes the complexity such that one can compare expressions found by PySR and those found in the literature?
For example: `model.complexity('1 + x_0 + x_1**2')`
This would allow the user to easily map the expressions found in the literature on the complexity vs accuracy plots.
Thank you in advance. | closed | 2023-05-28T10:07:22Z | 2024-03-24T23:45:31Z | https://github.com/MilesCranmer/PySR/issues/339 | [
"enhancement",
"priority: mid"
] | OsAmaro | 6 |
rougier/from-python-to-numpy | numpy | 39 | Incorrect Python version | Section 1.2 declares Python version 3.5.2 however section 2.1 "vectorized approach" uses random.choices() function which was introduced in Python 3.6.
https://docs.python.org/3.6/library/random.html#random.choices | closed | 2017-01-10T12:26:00Z | 2017-01-10T13:02:02Z | https://github.com/rougier/from-python-to-numpy/issues/39 | [] | reidfaiv | 2 |
deepfakes/faceswap | machine-learning | 1,255 | How to transfer facial expression of source image and other body gestures and posture of destination image. | Hi I really like your work. I wanted to know that is there any option in which we can transfer facial expression along with our face to the destination. in simple words I want a complete face projection to the destination frame. So that my facial expressions also swapped along with my face. Kindly let me know. | closed | 2022-08-12T09:24:11Z | 2022-08-12T09:32:00Z | https://github.com/deepfakes/faceswap/issues/1255 | [] | rohaantahir | 2 |
junyanz/pytorch-CycleGAN-and-pix2pix | computer-vision | 1,313 | Blurred results using cycleGAN on depthmaps | Hello everybody,
I want to use cycleGAN on depthmaps for domain adaptation. We are currently training the cycleGAN with two data sets of 2000 images each, but our result are blurred.
Trainining configuration:
image_size = 128
input_channels = 1
output_channels = 1
norm = instance_norm
lr = 0.0002
beta_1 = 0.5
cycle_loss_weight = 10
batch_size = 1
9 res_blocks
### Model
import torch
import torch.nn as nn
import torch.nn.functional as F
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.main = nn.Sequential(
nn.Conv2d(1, 64, 4, stride=2, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64, 128, 4, stride=2, padding=1),
nn.InstanceNorm2d(128),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(128, 256, 4, stride=2, padding=1),
nn.InstanceNorm2d(256),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(256, 512, 4, padding=1),
nn.InstanceNorm2d(512),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(512, 1, 4, padding=1),
)
def forward(self, x):
x = self.main(x)
x = F.avg_pool2d(x, x.size()[2:])
x = torch.flatten(x, 1)
return x
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.main = nn.Sequential(
# Initial convolution block
nn.ReflectionPad2d(3),
nn.Conv2d(1, 64, 7),
nn.InstanceNorm2d(64),
nn.ReLU(inplace=True),
# Downsampling
nn.Conv2d(64, 128, 3, stride=2, padding=1),
nn.InstanceNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 256, 3, stride=2, padding=1),
nn.InstanceNorm2d(256),
nn.ReLU(inplace=True),
# Residual blocks
ResidualBlock(256),
ResidualBlock(256),
ResidualBlock(256),
ResidualBlock(256),
ResidualBlock(256),
ResidualBlock(256),
ResidualBlock(256),
ResidualBlock(256),
ResidualBlock(256),
# Upsampling
nn.ConvTranspose2d(256, 128, 3, stride=2, padding=1, output_padding=1),
nn.InstanceNorm2d(128),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(128, 64, 3, stride=2, padding=1, output_padding=1),
nn.InstanceNorm2d(64),
nn.ReLU(inplace=True),
# Output layer
nn.ReflectionPad2d(3),
nn.Conv2d(64, 1, 7),
nn.Tanh()
)
def forward(self, x):
return self.main(x)
class ResidualBlock(nn.Module):
def __init__(self, in_channels):
super(ResidualBlock, self).__init__()
self.res = nn.Sequential(nn.ReflectionPad2d(1),
nn.Conv2d(in_channels, in_channels, 3),
nn.InstanceNorm2d(in_channels),
nn.ReLU(inplace=True),
nn.ReflectionPad2d(1),
nn.Conv2d(in_channels, in_channels, 3),
nn.InstanceNorm2d(in_channels))
def forward(self, x):
return x + self.res(x)
### Train
import argparse
import itertools
import os
import random
import torch.backends.cudnn as cudnn
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.utils as vutils
from PIL import Image
from tqdm import tqdm
import matplotlib.pylab as plt
from cyclegan_pytorch import DecayLR
from cyclegan_pytorch import Discriminator
from cyclegan_pytorch import Generator
from cyclegan_pytorch import ImageDataset
from cyclegan_pytorch import ReplayBuffer
from cyclegan_pytorch import weights_init
parser = argparse.ArgumentParser(
description="PyTorch implements `Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks`")
parser.add_argument("--dataroot", type=str, default="./data",
help="path to datasets. (default:./data)")
parser.add_argument("--dataset", type=str, default="depthmaps_white",
help="dataset name. (default:`horse2zebra`)"
"Option: [apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, "
"cezanne2photo, ukiyoe2photo, vangogh2photo, maps, facades, selfie2anime, "
"iphone2dslr_flower, ae_photos, ]")
parser.add_argument("--epochs", default=200, type=int, metavar="N",
help="number of total epochs to run")
parser.add_argument("--decay_epochs", type=int, default=100,
help="epoch to start linearly decaying the learning rate to 0. (default:100)")
parser.add_argument("-b", "--batch-size", default=1, type=int,
metavar="N",
help="mini-batch size (default: 1), this is the total "
"batch size of all GPUs on the current node when "
"using Data Parallel or Distributed Data Parallel")
parser.add_argument("--lr", type=float, default=0.0002,
help="learning rate. (default:0.0002)")
parser.add_argument("-p", "--print-freq", default=100, type=int,
metavar="N", help="print frequency. (default:100)")
parser.add_argument("--cuda", action="store_true", help="Enables cuda")
parser.add_argument("--netG_A2B", default="", help="path to netG_A2B (to continue training)")
parser.add_argument("--netG_B2A", default="", help="path to netG_B2A (to continue training)")
parser.add_argument("--netD_A", default="", help="path to netD_A (to continue training)")
parser.add_argument("--netD_B", default="", help="path to netD_B (to continue training)")
parser.add_argument("--image-size", type=int, default=128,
help="size of the data crop (squared assumed). (default:128)")
parser.add_argument("--outf", default="./outputs",
help="folder to output images. (default:`./outputs`).")
parser.add_argument("--manualSeed", type=int,
help="Seed for initializing training. (default:none)")
args = parser.parse_args()
print(args)
try:
os.makedirs(args.outf)
except OSError:
pass
try:
os.makedirs("weights")
except OSError:
pass
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
print("Random Seed: ", args.manualSeed)
random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
cudnn.benchmark = True
if torch.cuda.is_available() and not args.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
# Dataset
dataset = ImageDataset(root=os.path.join(args.dataroot, args.dataset),
transform=transforms.Compose([
transforms.Resize(int(args.image_size * 1.12), Image.BICUBIC),
transforms.RandomCrop(args.image_size),
#transforms.RandomHorizontalFlip(),
transforms.ToTensor()]),
#transforms.Grayscale(1),
#transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]),
#transforms.Normalize((0.5), (0.5))])
unaligned=True)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=True, pin_memory=True)
try:
os.makedirs(os.path.join(args.outf, args.dataset, "Sim"))
os.makedirs(os.path.join(args.outf, args.dataset, "Real"))
except OSError:
pass
try:
os.makedirs(os.path.join("weights", args.dataset))
except OSError:
pass
device = torch.device("cuda:0" if args.cuda else "cpu")
# create model
netG_A2B = Generator().to(device)
netG_B2A = Generator().to(device)
netD_A = Discriminator().to(device)
netD_B = Discriminator().to(device)
netG_A2B.apply(weights_init)
netG_B2A.apply(weights_init)
netD_A.apply(weights_init)
netD_B.apply(weights_init)
if args.netG_A2B != "":
netG_A2B.load_state_dict(torch.load(args.netG_A2B))
if args.netG_B2A != "":
netG_B2A.load_state_dict(torch.load(args.netG_B2A))
if args.netD_A != "":
netD_A.load_state_dict(torch.load(args.netD_A))
if args.netD_B != "":
netD_B.load_state_dict(torch.load(args.netD_B))
# define loss function (adversarial_loss) and optimizer
cycle_loss = torch.nn.L1Loss().to(device)
identity_loss = torch.nn.L1Loss().to(device)
adversarial_loss = torch.nn.MSELoss().to(device)
# Optimizers
optimizer_G = torch.optim.Adam(itertools.chain(netG_A2B.parameters(), netG_B2A.parameters()),
lr=args.lr, betas=(0.5, 0.999))
optimizer_D_A = torch.optim.Adam(netD_A.parameters(), lr=args.lr, betas=(0.5, 0.999))
optimizer_D_B = torch.optim.Adam(netD_B.parameters(), lr=args.lr, betas=(0.5, 0.999))
lr_lambda = DecayLR(args.epochs, 0, args.decay_epochs).step
lr_scheduler_G = torch.optim.lr_scheduler.LambdaLR(optimizer_G, lr_lambda=lr_lambda)
lr_scheduler_D_A = torch.optim.lr_scheduler.LambdaLR(optimizer_D_A, lr_lambda=lr_lambda)
lr_scheduler_D_B = torch.optim.lr_scheduler.LambdaLR(optimizer_D_B, lr_lambda=lr_lambda)
g_losses = []
d_losses = []
identity_losses = []
gan_losses = []
cycle_losses = []
fake_A_buffer = ReplayBuffer()
fake_B_buffer = ReplayBuffer()
for epoch in range(0, args.epochs):
progress_bar = tqdm(enumerate(dataloader), total=len(dataloader))
for i, data in progress_bar:
# get batch size data
real_image_A = data["Sim"].to(device)
real_image_B = data["Real"].to(device)
batch_size = real_image_A.size(0)
# real data label is 1, fake data label is 0.
real_label = torch.full((batch_size, 1), 1, device=device, dtype=torch.float32)
fake_label = torch.full((batch_size, 1), 0, device=device, dtype=torch.float32)
##############################################
# (1) Update G network: Generators A2B and B2A
##############################################
# Set G_A and G_B's gradients to zero
optimizer_G.zero_grad()
# Identity loss
# G_B2A(A) should equal A if real A is fed
identity_image_A = netG_B2A(real_image_A)
loss_identity_A = identity_loss(identity_image_A, real_image_A) * 1.0
# G_A2B(B) should equal B if real B is fed
identity_image_B = netG_A2B(real_image_B)
loss_identity_B = identity_loss(identity_image_B, real_image_B) * 1.0
# GAN loss
# GAN loss D_A(G_A(A))
fake_image_A = netG_B2A(real_image_B)
fake_output_A = netD_A(fake_image_A)
loss_GAN_B2A = adversarial_loss(fake_output_A, real_label)
# GAN loss D_B(G_B(B))
fake_image_B = netG_A2B(real_image_A)
fake_output_B = netD_B(fake_image_B)
loss_GAN_A2B = adversarial_loss(fake_output_B, real_label)
# Cycle loss
recovered_image_A = netG_B2A(fake_image_B)
loss_cycle_ABA = cycle_loss(recovered_image_A, real_image_A) * 10.0
recovered_image_B = netG_A2B(fake_image_A)
loss_cycle_BAB = cycle_loss(recovered_image_B, real_image_B) * 10.0
# Combined loss and calculate gradients
errG = loss_GAN_A2B + loss_GAN_B2A + loss_cycle_ABA + loss_cycle_BAB
#errG = loss_identity_A + loss_identity_B + loss_GAN_A2B + loss_GAN_B2A + loss_cycle_ABA + loss_cycle_BAB
# Calculate gradients for G_A and G_B
errG.backward()
# Update G_A and G_B's weights
optimizer_G.step()
##############################################
# (2) Update D network: Discriminator A
##############################################
# Set D_A gradients to zero
optimizer_D_A.zero_grad()
# Real A image loss
real_output_A = netD_A(real_image_A)
errD_real_A = adversarial_loss(real_output_A, real_label)
# Fake A image loss
fake_image_A = fake_A_buffer.push_and_pop(fake_image_A)
fake_output_A = netD_A(fake_image_A.detach())
errD_fake_A = adversarial_loss(fake_output_A, fake_label)
# Combined loss and calculate gradients
errD_A = (errD_real_A + errD_fake_A) / 2
# Calculate gradients for D_A
errD_A.backward()
# Update D_A weights
optimizer_D_A.step()
##############################################
# (3) Update D network: Discriminator B
##############################################
# Set D_B gradients to zero
optimizer_D_B.zero_grad()
# Real B image loss
real_output_B = netD_B(real_image_B)
errD_real_B = adversarial_loss(real_output_B, real_label)
# Fake B image loss
fake_image_B = fake_B_buffer.push_and_pop(fake_image_B)
fake_output_B = netD_B(fake_image_B.detach())
errD_fake_B = adversarial_loss(fake_output_B, fake_label)
# Combined loss and calculate gradients
errD_B = (errD_real_B + errD_fake_B) / 2
# Calculate gradients for D_B
errD_B.backward()
# Update D_B weights
optimizer_D_B.step()
progress_bar.set_description(
f"[{epoch}/{args.epochs - 1}][{i}/{len(dataloader) - 1}] "
f"Loss_D: {(errD_A + errD_B).item():.4f} "
f"Loss_G: {errG.item():.4f} "
f"Loss_G_identity: {(loss_identity_A + loss_identity_B).item():.4f} "
f"loss_G_GAN: {(loss_GAN_A2B + loss_GAN_B2A).item():.4f} "
f"loss_G_cycle: {(loss_cycle_ABA + loss_cycle_BAB).item():.4f}")
if i % args.print_freq == 0:
vutils.save_image(real_image_A,
f"{args.outf}/{args.dataset}/Sim/real_samples.png",
normalize=True)
vutils.save_image(real_image_B,
f"{args.outf}/{args.dataset}/Real/real_samples.png",
normalize=True)
#print('Real Image A Shape:', real_image_A.shape)
#print('Real Image B Shape:', real_image_B.shape)
fake_image_A = 0.5 * (netG_B2A(real_image_B).data + 1.0)
fake_image_B = 0.5 * (netG_A2B(real_image_A).data + 1.0)
#print('Fake Image A Shape:',fake_image_A.shape)
#print('Fake Image B Shape:',fake_image_B.shape)
vutils.save_image(fake_image_A.detach(),
f"{args.outf}/{args.dataset}/Sim/fake_samples.png",
normalize=True)
vutils.save_image(fake_image_B.detach(),
f"{args.outf}/{args.dataset}/Real/fake_samples.png",
normalize=True)
transformed_image_A = 0.5 * (netG_B2A(fake_image_B).data + 1.0)
transformed_image_B = 0.5 * (netG_A2B(fake_image_A).data + 1.0)
#print('Transformed Image A Shape:',transformed_image_A.shape)
#print('Transformed Image B Shape:',transformed_image_B.shape)
vutils.save_image(transformed_image_A.detach(),
f"{args.outf}/{args.dataset}/Sim/transformed_samples.png",
normalize=True)
vutils.save_image(transformed_image_B.detach(),
f"{args.outf}/{args.dataset}/Real/transformed_samples.png",
normalize=True)
# merge results
new_img = Image.new('RGB', (args.image_size*3,args.image_size*2),'white')
real_sample_A = Image.open(f"{args.outf}/{args.dataset}/Sim/real_samples.png")
fake_sample_B = Image.open(f"{args.outf}/{args.dataset}/Real/fake_samples.png")
transformed_image_A = Image.open(f"{args.outf}/{args.dataset}/Sim/transformed_samples.png")
transformed_image_B = Image.open(f"{args.outf}/{args.dataset}/Real/transformed_samples.png")
real_sample_B = Image.open(f"{args.outf}/{args.dataset}/Real/real_samples.png")
fake_sample_A = Image.open(f"{args.outf}/{args.dataset}/Sim/fake_samples.png")
new_img.paste(real_sample_A,(0,0,args.image_size,args.image_size))
new_img.paste(fake_sample_B,(args.image_size,0,args.image_size*2,args.image_size))
new_img.paste(transformed_image_A,(args.image_size*2,0,args.image_size*3,args.image_size))
new_img.paste(real_sample_B,(0,args.image_size,args.image_size,args.image_size*2))
new_img.paste(fake_sample_A,(args.image_size,args.image_size,args.image_size*2,args.image_size*2))
new_img.paste(transformed_image_B,(args.image_size*2,args.image_size,args.image_size*3,args.image_size*2))
new_img.save(f"{args.outf}/{args.dataset}/results_at_epoch_{epoch}_idx_{i}.png")
# do check pointing
torch.save(netG_A2B.state_dict(), f"weights/{args.dataset}/netG_A2B_epoch_{epoch}.pth")
torch.save(netG_B2A.state_dict(), f"weights/{args.dataset}/netG_B2A_epoch_{epoch}.pth")
torch.save(netD_A.state_dict(), f"weights/{args.dataset}/netD_A_epoch_{epoch}.pth")
torch.save(netD_B.state_dict(), f"weights/{args.dataset}/netD_B_epoch_{epoch}.pth")
# Update learning rates
lr_scheduler_G.step()
lr_scheduler_D_A.step()
lr_scheduler_D_B.step()
# save last check pointing
torch.save(netG_A2B.state_dict(), f"weights/{args.dataset}/netG_A2B.pth")
torch.save(netG_B2A.state_dict(), f"weights/{args.dataset}/netG_B2A.pth")
torch.save(netD_A.state_dict(), f"weights/{args.dataset}/netD_A.pth")
torch.save(netD_B.state_dict(), f"weights/{args.dataset}/netD_B.pth")
| open | 2021-09-05T08:12:41Z | 2021-10-27T20:17:57Z | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/1313 | [] | ghost | 1 |
jupyterhub/repo2docker | jupyter | 476 | Julia support does not require `environment.yml` | The documentation for how to use Julia states that you need a `REQUIRE` file as well as an `environment.yml`. This is no longer correct. Should be fixed by removing the comment about the `environment.yml`.
https://repo2docker.readthedocs.io/en/latest/config_files.html#require-install-a-julia-environment
This got changed in #103 but docs didn't get updated. | closed | 2018-11-24T15:34:19Z | 2018-11-27T07:40:56Z | https://github.com/jupyterhub/repo2docker/issues/476 | [] | betatim | 0 |
Netflix/metaflow | data-science | 1,513 | Support explicit job queues for @kubernetes | tracking integration work with kueue/volcano/yunicorn
cc @rparundekar | open | 2023-08-29T16:34:20Z | 2023-08-29T16:34:20Z | https://github.com/Netflix/metaflow/issues/1513 | [] | savingoyal | 0 |
sqlalchemy/sqlalchemy | sqlalchemy | 12,042 | Support adding where condition to UniqueConstraint | ### Describe the use case
I want to add a where condition to UniqueConstraint so that the constraint can be applied to a selected set of records. I know this can be done using Index but I want to defer this constraint till the transaction is completed. I cannot defer the Index nor I can add where condition to UniqueConstraint.
Django seems to support adding where condition to UniqueConstraint. I would really appreciate having this feature in SQLAlchemy since it solves a common problem in efficient way.
### Databases / Backends / Drivers targeted
I'm looking this for PostgreSQL
### Example Use
In Django, this is supported like this:
```
models.UniqueConstraint(
fields=["email", "tenant"],
condition=Q(deleted_at=None),
name="users_email_tenant_id_deleted_at_null_uniq",
deferrable=Deferrable.DEFERRED,
),
```
Here, I'm applying the constraint on all the records which are not deleted and also deferring the check so that I can do bulk updates.
### Additional context
_No response_ | closed | 2024-10-29T17:24:54Z | 2024-10-29T17:36:49Z | https://github.com/sqlalchemy/sqlalchemy/issues/12042 | [
"postgresql"
] | cankush625 | 1 |
wandb/wandb | tensorflow | 9,425 | [Bug]: Logging does not update progress bar | ### Describe the bug
The logs interface does not update `tqdm` progress bars.
### Reproducing
With the following code,
```python
import wandb
import tqdm
import time
run = wandb.init(project="tqdm-test")
for i in tqdm.tqdm(range(1000), ncols=88, ascii=True):
run.log({"acc": i / 1000})
time.sleep(10)
run.finish()
```
the progress bar looks like
```
1%|6 | 13/1000 [02:10<2:44:31, 10.00s/it]
```
But the logs tab in the web interface always shows
```
1 0%| | 0/1000 [00:00<?, ?it/s]
```
> See https://wandb.ai/francois-rozet/tqdm-test/runs/rlqhqf9v/logs | open | 2025-02-06T14:35:20Z | 2025-02-26T16:12:26Z | https://github.com/wandb/wandb/issues/9425 | [
"ty:bug",
"a:app"
] | francois-rozet | 10 |
modelscope/modelscope | nlp | 438 | img2video | does modelscope do img2video similar to runway gn2 ? | closed | 2023-08-03T20:26:02Z | 2023-09-18T11:24:09Z | https://github.com/modelscope/modelscope/issues/438 | [] | almfahd41 | 3 |
iperov/DeepFaceLab | deep-learning | 5,734 | Eye and mouth priority bad after 144k | Expected behavior: eyes and mouth conform to src image.
Actually: generated face keeps reverting to a generalized face, often not even close to the direction of the src, It will stat looking like what it is supposed to but then revert. I am on 144k iter and my faces are wrong.
Expected: Scr & Dst images to become defined and clear to see in training.
Actually: After 144k iter src & dts models are still very blurry and occationally warp to odd dimensions that carry over to the combined image.
Dims are all default values, mask training on, eye and mouth priority on, warp on. | open | 2023-10-07T13:04:04Z | 2023-10-07T13:10:48Z | https://github.com/iperov/DeepFaceLab/issues/5734 | [] | D-reads | 0 |
torchbox/wagtail-grapple | graphql | 69 | Confusing docstring in PageInterface | The docstring of `resolve_children()` reads: "Resolves a list of live children of this page with `show_in_menus` set." (I guess [this](https://docs.wagtail.io/en/stable/reference/pages/queryset_reference.html#examples) is the source of the docstring). The function however, does not filter live child pages, nor does it filter on `show_in_menus`.
I wouldn't mind making a PR to resolve this issue, but I don't know if this function should be returning live pages only (filtering for `show_in_menus` does not make sense here IMO).
Is the docstring or the query invalid?
**Useful links:**
- https://github.com/torchbox/wagtail-grapple/blob/master/grapple/types/pages.py#L65-L70 | closed | 2020-04-30T14:22:58Z | 2020-08-10T21:25:40Z | https://github.com/torchbox/wagtail-grapple/issues/69 | [] | leewesleyv | 1 |
tatsu-lab/stanford_alpaca | deep-learning | 128 | 轻量化专属化ChayGpt | open | 2023-03-23T02:42:45Z | 2023-03-23T02:42:45Z | https://github.com/tatsu-lab/stanford_alpaca/issues/128 | [] | vampirelzy | 0 | |
httpie/cli | api | 1,036 | Following Link-Header / MultiPage-Requests (RFC5988) | Please implement RFC5988 Link Header
https://tools.ietf.org/html/rfc5988#section-5
Also see: https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Link
I often do need this functionality.
And yes, I am know of:
https://github.com/httpie/httpie/issues/323 | open | 2021-02-18T08:52:42Z | 2021-12-28T10:38:33Z | https://github.com/httpie/cli/issues/1036 | [
"enhancement",
"needs product design",
"deferred"
] | blurayne | 2 |
ipython/ipython | jupyter | 13,913 | Changing `c.TerminalInteractiveShell.autosuggestions_provider` in the configuration file will cause IPython shell to fail to start | <!-- This is the repository for IPython command line, if you can try to make sure this question/bug/feature belong here and not on one of the Jupyter repositories.
If it's a generic Python/Jupyter question, try other forums or discourse.jupyter.org.
If you are unsure, it's ok to post here, though, there are few maintainer so you might not get a fast response.
-->
IPython version: 8.9.0, Python version: CPython 3.10.9
The default value of the configuration item `c.TerminalInteractiveShell.autosuggestions_provider` is `'NavigableAutoSuggestFromHistory'`. And the documentation said:
> \## Specifies from which source automatic suggestions are provided. Can be set to
> \# ``'NavigableAutoSuggestFromHistory'`` (:kbd:`up` and :kbd:`down` swap
> \# suggestions), ``'AutoSuggestFromHistory'``, or ``None`` to disable automatic
> \# suggestions. Default is `'NavigableAutoSuggestFromHistory`'.
> \# Default: 'NavigableAutoSuggestFromHistory'
But if you set it to `'AutoSuggestFromHistory'` or `None` will cause IPython shell to fail to start (Extra-detailed tracebacks is enabled):
```
$ > ipython
Error in sys.excepthook:
Traceback (most recent call last):
File "/usr/lib/python3.10/pathlib.py", line 1305, in is_dir
return S_ISDIR(self.stat().st_mode)
AttributeError: 'str' object has no attribute 'stat'
Original exception was:
Traceback (most recent call last):
File "/usr/bin/ipython", line 8, in <module>
sys.exit(start_ipython())
File "/usr/lib/python3.10/site-packages/IPython/__init__.py", line 124, in start_ipython
return launch_new_instance(argv=argv, **kwargs)
File "/usr/lib/python3.10/site-packages/traitlets/config/application.py", line 1040, in launch_instance
app.initialize(argv)
File "/usr/lib/python3.10/site-packages/traitlets/config/application.py", line 113, in inner
return method(app, *args, **kwargs)
File "/usr/lib/python3.10/site-packages/IPython/terminal/ipapp.py", line 279, in initialize
self.init_shell()
File "/usr/lib/python3.10/site-packages/IPython/terminal/ipapp.py", line 293, in init_shell
self.shell = self.interactive_shell_class.instance(parent=self,
File "/usr/lib/python3.10/site-packages/traitlets/config/configurable.py", line 551, in instance
inst = cls(*args, **kwargs)
File "/usr/lib/python3.10/site-packages/IPython/terminal/interactiveshell.py", line 687, in __init__
super(TerminalInteractiveShell, self).__init__(*args, **kwargs)
File "/usr/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 560, in __init__
super(InteractiveShell, self).__init__(**kwargs)
File "/usr/lib/python3.10/site-packages/traitlets/config/configurable.py", line 109, in __init__
self.config = config
File "/usr/lib/python3.10/site-packages/traitlets/traitlets.py", line 732, in __set__
self.set(obj, value)
File "/usr/lib/python3.10/site-packages/traitlets/traitlets.py", line 721, in set
obj._notify_trait(self.name, old_value, new_value)
File "/usr/lib/python3.10/site-packages/traitlets/traitlets.py", line 1505, in _notify_trait
self.notify_change(
File "/usr/lib/python3.10/site-packages/traitlets/traitlets.py", line 1517, in notify_change
return self._notify_observers(change)
File "/usr/lib/python3.10/site-packages/traitlets/traitlets.py", line 1564, in _notify_observers
c(event)
File "/usr/lib/python3.10/site-packages/traitlets/traitlets.py", line 1146, in compatible_observer
return func(self, change)
File "/usr/lib/python3.10/site-packages/traitlets/config/configurable.py", line 218, in _config_changed
self._load_config(change.new, traits=traits, section_names=section_names)
File "/usr/lib/python3.10/site-packages/traitlets/config/configurable.py", line 167, in _load_config
with self.hold_trait_notifications():
File "/usr/lib/python3.10/contextlib.py", line 142, in __exit__
next(self.gen)
File "/usr/lib/python3.10/site-packages/traitlets/traitlets.py", line 1502, in hold_trait_notifications
self.notify_change(change)
File "/usr/lib/python3.10/site-packages/traitlets/traitlets.py", line 1517, in notify_change
return self._notify_observers(change)
File "/usr/lib/python3.10/site-packages/traitlets/traitlets.py", line 1564, in _notify_observers
c(event)
File "/usr/lib/python3.10/site-packages/IPython/terminal/interactiveshell.py", line 417, in _autosuggestions_provider_changed
self._set_autosuggestions(provider)
File "/usr/lib/python3.10/site-packages/IPython/terminal/interactiveshell.py", line 399, in _set_autosuggestions
if self.auto_suggest and isinstance(
AttributeError: 'TerminalInteractiveShell' object has no attribute 'auto_suggest'
``` | closed | 2023-01-28T16:42:07Z | 2023-01-30T13:02:04Z | https://github.com/ipython/ipython/issues/13913 | [
"bug",
"autosuggestions"
] | nukemiko | 1 |
awesto/django-shop | django | 236 | How to integrate it to django-cms? | Sorry to interrupt you, I searched the web but didn't find answer to this question. How to make it an extension of django cms? Thanks in advance!
| closed | 2013-06-26T14:05:39Z | 2016-02-02T13:58:57Z | https://github.com/awesto/django-shop/issues/236 | [] | educate | 4 |
NullArray/AutoSploit | automation | 1,067 | what's wrong ? | [+] attempting to load API keys
[+] Shodan API token loaded from /root/Desktop/AutoSploit-master/etc/tokens/shodan.key
[+] Censys API token loaded from /root/Desktop/AutoSploit-master/etc/tokens/censys.key
[-] no arguments have been parsed at run time, dropping into terminal session. to get help type `help` to quit type `exit/quit` to get help on a specific command type `command help`
| closed | 2019-05-01T14:46:45Z | 2019-05-02T18:12:55Z | https://github.com/NullArray/AutoSploit/issues/1067 | [] | lmx5200410 | 4 |
gradio-app/gradio | machine-learning | 10,663 | Search functionality doesn't work for gr.Dataframe | ### Describe the bug
Notice another bug for gr.Dataframe, i.e., the search & filter functionality doesn't work for text cells.
<img width="1920" alt="Image" src="https://github.com/user-attachments/assets/8f8f180d-451f-4b1c-842d-525cefee8f37" />
<img width="1920" alt="Image" src="https://github.com/user-attachments/assets/89a99660-7b4a-4af3-8cb4-f3e105c37456" />
<img width="1920" alt="Image" src="https://github.com/user-attachments/assets/c3292e86-e5d1-4c7b-8b32-f2ee4f0d66eb" />
### Have you searched existing issues? 🔎
- [x] I have searched and found no existing issues
### Reproduction
```python
import pandas as pd
import gradio as gr
# Creating a sample dataframe
def run():
df = pd.DataFrame({
"A" : ["Apparel & Accessories", "Home & Garden", "Health & Beauty", "Cameras & Optics", "Apparel & Accessories"],
"B" : [6, 2, 54, 3, 2],
"C" : [3, 20, 7, 3, 8],
"D" : [2, 3, 6, 2, 6],
"E" : [-1, 45, 64, 32, 23]
})
df = df.style.map(color_num, subset=["E"])
return df
# Function to apply text color
def color_num(value: float) -> str:
color = "red" if value >= 0 else "green"
color_style = "color: {}".format(color)
return color_style
# Displaying the styled dataframe in Gradio
with gr.Blocks() as demo:
gr.Textbox("{}".format(gr.__version__))
a = gr.DataFrame(show_search="search")
b = gr.Button("run")
b.click(run,outputs=a)
demo.launch()
```
### Screenshot
_No response_
### Logs
```shell
```
### System Info
```shell
gradio = 5.17.1
```
### Severity
Blocking usage of gradio | closed | 2025-02-24T02:54:23Z | 2025-03-10T18:14:59Z | https://github.com/gradio-app/gradio/issues/10663 | [
"bug",
"💾 Dataframe"
] | jamie0725 | 4 |
laughingman7743/PyAthena | sqlalchemy | 493 | Add support for Spark calculations | # Description
Add support to run spark calculations using any cursor
# Related docs
1. Implementation in awswrangler - https://github.com/aws/aws-sdk-pandas/blob/main/awswrangler/athena/_spark.py
2. Official AWS docs - https://docs.aws.amazon.com/athena/latest/ug/notebooks-spark.html
3. MR in dbt-athena-community - https://github.com/dbt-athena/dbt-athena/pull/248
# Comments
I am currently working on adding support to run python models using the dbt-athena-community adapter and it would be much easier to accomplish if the pyathena library supports this first. I don't think mock_athena supports these yet so testing it actually much more difficult than I thought. | closed | 2023-11-23T17:29:36Z | 2024-01-09T05:50:02Z | https://github.com/laughingman7743/PyAthena/issues/493 | [] | Avinash-1394 | 8 |
msoedov/langcorn | rest-api | 20 | langchain Agents | Thanks for the great repository.
Does langcorn support langchain agents? | open | 2023-06-20T10:36:55Z | 2023-08-21T15:05:44Z | https://github.com/msoedov/langcorn/issues/20 | [] | zlapp | 7 |
SYSTRAN/faster-whisper | deep-learning | 781 | Problem with audio | When I cut out only 5 minutes of the movie, everything worked correctly (screen #1).
The one hour portion of the movie caused the error (screen #2).
General
Unique ID : 80764340276249783376247501835629204513 (0x3CC2A569474575CF3E3F1C6E312F9421)
Complete name : C:\Whisper-Faster\55776.mkv
Format : Matroska
Format version : Version 4
File size : 9.92 GiB
Duration : 1 h 0 min
Overall bit rate mode : Variable
Overall bit rate : 23.7 Mb/s
Encoded date : UTC 2024-04-05 09:25:18
Writing application : mkvmerge v82.0 ('I'm The President') 64-bit
Writing library : libebml v1.4.5 + libmatroska v1.7.1
Video
ID : 1
Format : AVC
Format/Info : Advanced Video Codec
Format profile : High@L4.1
Format settings : CABAC / 2 Ref Frames
Format settings, CABAC : Yes
Format settings, Reference fra : 2 frames
Format settings, GOP : M=1, N=10
Codec ID : V_MPEG4/ISO/AVC
Duration : 1 h 0 min
Bit rate mode : Variable
Bit rate : 19.9 Mb/s
Width : 1 920 pixels
Height : 1 080 pixels
Display aspect ratio : 16:9
Frame rate mode : Constant
Frame rate : 23.976 (23976/1000) FPS
Original frame rate : 23.976 (24000/1001) FPS
Color space : YUV
Chroma subsampling : 4:2:0
Bit depth : 8 bits
Scan type : Progressive
Bits/(Pixel*Frame) : 0.400
Stream size : 8.34 GiB (84%)
Default : Yes
Forced : No
Audio #1
ID : 2
Format : DTS XLL
Format/Info : Digital Theater Systems
Commercial name : DTS-HD Master Audio
Codec ID : A_DTS
Duration : 1 h 0 min
Bit rate mode : Variable
Bit rate : 1 144 kb/s
Channel(s) : 2 channels
Channel layout : L R
Sampling rate : 48.0 kHz
Frame rate : 93.750 FPS (512 SPF)
Bit depth : 24 bits
Compression mode : Lossless
Stream size : 491 MiB (5%)
Default : Yes
Forced : No
Audio #2
ID : 3
Format : DTS XLL
Format/Info : Digital Theater Systems
Commercial name : DTS-HD Master Audio
Codec ID : A_DTS
Duration : 1 h 0 min
Bit rate mode : Variable
Bit rate : 2 496 kb/s
Channel(s) : 6 channels
Channel layout : C L R Ls Rs LFE
Sampling rate : 48.0 kHz
Frame rate : 93.750 FPS (512 SPF)
Bit depth : 24 bits
Compression mode : Lossless
Stream size : 1.05 GiB (11%)
Default : Yes
Forced : No
Text #1
ID : 4
Format : PGS
Muxing mode : zlib
Codec ID : S_HDMV/PGS
Codec ID/Info : Picture based subtitle format used on BDs/HD-DVDs
Duration : 59 min 30 s
Bit rate : 209 kb/s
Frame rate : 1.482 FPS
Count of elements : 5291
Stream size : 89.0 MiB (1%)
Default : Yes
Forced : No
Text #2
ID : 5
Format : PGS
Muxing mode : zlib
Codec ID : S_HDMV/PGS
Codec ID/Info : Picture based subtitle format used on BDs/HD-DVDs
Duration : 59 min 30 s
Bit rate : 35.3 kb/s
Frame rate : 0.459 FPS
Count of elements : 1638
Stream size : 15.0 MiB (0%)
Language : Chinese
Default : Yes
Forced : No
Text #3
ID : 6
Format : PGS
Muxing mode : zlib
Codec ID : S_HDMV/PGS
Codec ID/Info : Picture based subtitle format used on BDs/HD-DVDs
Duration : 59 min 30 s
Bit rate : 35.7 kb/s
Frame rate : 0.459 FPS
Count of elements : 1638
Stream size : 15.2 MiB (0%)
Language : Chinese
Default : Yes
Forced : No
| closed | 2024-04-05T11:41:34Z | 2024-11-14T13:32:46Z | https://github.com/SYSTRAN/faster-whisper/issues/781 | [] | Herzfrequenz21 | 5 |
onnx/onnx | deep-learning | 6,301 | Split onnx model in architecture and weights | ## Context
Among others "THE MODEL OPENNESS FRAMEWORK: PROMOTING COMPLETENESS AND OPENNESS FOR REPRODUCIBILITY,
TRANSPARENCY, AND USABILITY IN ARTIFICIAL INTELLIGENCE" (https://arxiv.org/pdf/2403.13784) is in favor of the separation between architecture and model weights (https://arxiv.org/pdf/2403.13784 p.8 l.10)
As model parameters are in fact data, there is discussion whether data-related licenses such as CDLA-Permissive-2.0
might be better suited for the weights (https://arxiv.org/pdf/2403.13784 p.8 l.3)
## Question
Regardless of this, the question for me is whether there is a converter or workflow with which I can, for example, split a finished Onnx model into a file that only describes the architecture (pbtxt?) and the weights? Similar to intel openvino (https://docs.openvino.ai/nightly/documentation/openvino-ir-format.html)
If not, what is missing at the moment? | open | 2024-08-16T17:39:53Z | 2024-08-26T12:13:22Z | https://github.com/onnx/onnx/issues/6301 | [] | andife | 2 |
plotly/dash | dash | 2,696 | [Feature Request] Make `dcc.Loading` toggle-able attribute for enabled/disabled | I have created an application where various `dcc.Store` elements are used to hold user-imported hash tables for images of varying sizes. These images can then be blended together and viewed in a `dcc.Graph`.
Based on the size and number of images, I would like to be able to wrap these store elements in `dcc.Loading` components whose visibility/functionality is toggle-able i.e. if the user wishes, he/she can either enable or disable the loading visibility for the stores using a Dash toggle switch. Right now, I don't see an option for this because there is no `visibility` or `enabled` attribute for the Loading component. The result is that the visibility for the stores must be set when the app is executed but cannot be dynamically changed within the session. It would be very beneficial to have an attribute for enabled or visible, similar to how some other dash components have, in order to dynamically toggle the visibility of loadings within the app on the fly.
| closed | 2023-11-20T18:30:42Z | 2024-06-03T17:32:26Z | https://github.com/plotly/dash/issues/2696 | [
"feature",
"P3"
] | matt-sd-watson | 8 |
tensorflow/tensor2tensor | deep-learning | 1,077 | How to run t2t-decode with mnist model? | ### Description
I have trained a mnist model follow the quick start guide successfully.
Now I want to test it by my test images. But I don't know how to write t2t-decode script.
I used
-----------------------------------
t2t-decoder \
--data_dir=~/t2t_data \
--problem=image_mnist \
--model=shake_shake \
--hparams_set=shake_shake_quick \
--output_dir=~/t2t_train/mnist \
--decode_from_dataset=/home/admin1/t2t/test/t10k-images.idx3-ubyte \
--decode_to_file=image.label
--------------------------------------
--------------------------------------
t2t-decoder \
--data_dir=~/t2t_data \
--problem=image_mnist \
--model=shake_shake \
--hparams_set=shake_shake_quick \
--output_dir=~/t2t_train/mnist \
--decode_from_file=/home/admin1/t2t/test/502.bmp \
--decode_to_file=image.label
--------------------------------------
but both scripts are failed.
So could anyone provide the right one to run it? Thanks. | open | 2018-09-19T07:49:59Z | 2018-09-19T07:49:59Z | https://github.com/tensorflow/tensor2tensor/issues/1077 | [] | strongwdl | 0 |
521xueweihan/HelloGitHub | python | 2,193 | 超轻量级前端开发框架：Lightue | ## 项目推荐
- 项目地址：
https://github.com/smalllong/lightue
- 类别：JS
- 项目后续更新计划：
- 创建官方详细入门使用文档
- 继续加入各种框架级功能如生命周期、配套router等
- 项目描述：
- Lightue是一款超轻量级前端框架，类似Vue、React，但是体积仅不到2KB（min+br）。它让你可以用现代的方式创建网页：声明状态，更改状态后框架可以自动更新dom，将页面拆分成很多函数组件以复用等。项目学习及使用成本极低，提供的对象式模板声明写法让使用者可以无需编译便可采用类似jsx的方式编写页面。
- 目前尚未成熟到可以支持大型项目的程度，不过可以用来搭建一些自己的小型项目，或在大型项目的局部使用。初学者学习后可以快速搭建自己的页面。框架源码仅300行左右，初学者也可以自己查看源码，从而学习框架的原理。
- 推荐理由：轻量小巧，上手简单，创新型设计
- 示例代码：（可选）长度：1-20 行
```js
// specify application state
var S = Lightue.useState({
text: 'Hello world!'
})
// create Lightue instance
var vm = Lightue({
// for static content just specify statically
hi: 'Hi world!',
// for dynamic content that depends on state, use a state function (a function that uses state)
hello: () => S.text
}, {
el: '#app' // append to <div id='app'></div>
})
// change the state after 2 seconds and the DOM automatically get updated
setTimeout(function() {
S.text = 'Hello again!'
}, 2000)
```
- 其他示例
- https://codepen.io/lxl898/pen/vYyooWK
- https://smalllong.github.io/spreadsheet/index.html
| closed | 2022-05-07T12:38:33Z | 2022-05-25T09:40:51Z | https://github.com/521xueweihan/HelloGitHub/issues/2193 | [
"JavaScript 项目"
] | smalllong | 1 |
pytest-dev/pytest-selenium | pytest | 9 | Review Opera driver support | Opera has changed substantially since I last tested it, and I suspect that we may need to improve the experience. At very least we should provide a decent guide to getting Opera configured for running tests. We should also see if there's a way to get the Opera tests running on Travis-CI.
| closed | 2015-06-11T10:38:36Z | 2015-06-26T16:10:12Z | https://github.com/pytest-dev/pytest-selenium/issues/9 | [] | davehunt | 2 |
errbotio/errbot | automation | 736 | No error message on plugin configuration exception | When plugin configuration fails (`check_configuration` raises a `errbot.utils.ValidationException`), I still get the message "Plugin configuration done.".
There should be an error message to the user instead.
Example:
```
>>> !plugin config exec {'command': 'failure'}
20:42:25 INFO errbot.plugins.ACLS Matching ACL {} against username gbin@localhost for command Plugins:plugin_config
20:42:25 INFO errbot.plugins.ACLS Check if plugin_config is admin only command.
20:42:25 INFO errbot.errBot Processing command 'plugin_config' with parameters 'exec {'command': 'failure'}' from gbin@localhost
20:42:26 INFO errbot.plugin_manager Activating exec with min_err_version = 4.0.0 and max_version = 4.0.99
20:42:26 ERROR errbot.plugin_manager Something is wrong with the configuration of the plugin exec
Traceback (most recent call last):
File "/usr/local/lib/python2.7/dist-packages/errbot/plugin_manager.py", line 277, in activate_plugin_with_version_check
obj.check_configuration(config)
File "/home/cweiske/Dev/tools/xmppexecbot/exec.py", line 44, in check_configuration
self.executable_exists(configuration['command'])
File "/home/cweiske/Dev/tools/xmppexecbot/exec.py", line 31, in executable_exists
raise ValidationException('Command not in PATH')
ValidationException: Command not in PATH
20:42:26 ERROR errbot.plugin_manager Error loading exec
Traceback (most recent call last):
File "/usr/local/lib/python2.7/dist-packages/errbot/plugin_manager.py", line 452, in activate_plugin
self.activate_plugin_with_version_check(name, self.get_plugin_configuration(name))
File "/usr/local/lib/python2.7/dist-packages/errbot/plugin_manager.py", line 283, in activate_plugin_with_version_check
raise PluginConfigurationException(unicode(ex))
PluginConfigurationException: Command not in PATH
╌╌[MD ]╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
Plugin configuration done.
```
| closed | 2016-05-04T18:50:54Z | 2016-05-06T23:48:56Z | https://github.com/errbotio/errbot/issues/736 | [
"feature: plugins",
"#usability"
] | cweiske | 2 |
iperov/DeepFaceLab | machine-learning | 5,536 | Question about image loading and SampleLoader.py | I'm trying to modify the RGB data that is loaded into DFL slightly, e.g. add some text overlay etc.
I found the SampleLoader.py to be the ideal candidate but I fail to understand where exactly the RGB data is. I see this code here:
```
def process_data(self, data):
idx, filename = data
io.log_info("process_data: " + filename)
dflimg = DFLIMG.load (Path(filename))
if dflimg is None or not dflimg.has_data():
self.log_err (f"FaceSamplesLoader: {filename} is not a dfl image file.")
data = None
else:
data = (dflimg.get_face_type(),
dflimg.get_shape(),
dflimg.get_landmarks(),
dflimg.get_seg_ie_polys(),
dflimg.get_xseg_mask_compressed(),
dflimg.get_eyebrows_expand_mod(),
dflimg.get_source_filename() )
return idx, data
```
but the return value `data` does not contain RGB data of the file or am I missing something? DFLJGP has several fields: `img` `chunks` `data` but none of them are used here...
I'm trying to display some text in the trainer and I thought it would be easiest to modify the images on load for that.
| closed | 2022-07-15T21:12:43Z | 2022-07-17T13:02:13Z | https://github.com/iperov/DeepFaceLab/issues/5536 | [] | zqueezy | 1 |
gunthercox/ChatterBot | machine-learning | 1,935 | django.db.utils.OperationalError: (1071, 'Specified key was too long; max key length is 767 bytes') | I am using Python 3.8 on ubuntu 18.04. While running sudo python3 manage.py migrate I am getting the following error. Other tables are migrated successfully except django_chatterbot
Applying django_chatterbot.0010_statement_text...Traceback (most recent call last):
....
....
File "/usr/local/lib/python3.8/site-packages/MySQLdb/connections.py", line 239, in query
_mysql.connection.query(self, query)
django.db.utils.OperationalError: (1071, 'Specified key was too long; max key length is 767 bytes')
[error.txt](https://github.com/gunthercox/ChatterBot/files/4373537/error.txt)
| open | 2020-03-24T06:33:03Z | 2020-03-24T06:33:03Z | https://github.com/gunthercox/ChatterBot/issues/1935 | [] | bktbunu | 0 |
Teemu/pytest-sugar | pytest | 186 | No instant stack trace displayed when test fails and tests are run with Gitlab CI | Env issue | closed | 2020-02-25T14:03:31Z | 2020-02-27T10:03:30Z | https://github.com/Teemu/pytest-sugar/issues/186 | [] | fbarbu15 | 1 |
pydata/xarray | numpy | 9,478 | TreeNode constructor should not modify children in-place | ### What is your issue?
The in-place modification issue described in #9196 was fixed for `DataTree`, but actually still exists for its private base class `TreeNode`.
```python
In [1]: from xarray.core.treenode import TreeNode
...:
...: child = TreeNode()
...: root = TreeNode(children={'child': child})
...: print(child.parent)
TreeNode(children={'child': TreeNode(children={})})
```
(The issue here being that the constructor has returned a new object `root` but also modified the object `child` in-place.)
We do test `TreeNode` directly (in `test_treenode.py`), which I think is useful as it internally separates tree structure operations from data manipulation operations. But if you copy the [new test](https://github.com/pydata/xarray/blob/781877cb76dd2806dbefded817ce7e012f5a4c2e/xarray/tests/test_datatree.py#L48) added in #9196 to `treenode.py` it will currently fail.
Trying to fix this reveals a rabbit hole of ways in which the `DataTree`/`TreeNode` relationship should be refactored:
1. `DataTree.__init__` should call `TreeNode.__init__` to assign `children`,
2. `TreeNode.__init__` should `.copy()` children (i.e. move the solution to #9196 down into `TreeNode`),
3. This requires `.copy()` to be defined on `TreeNode` rather than on `DataTree`, with only `._copy_node` overridden to also copy `.data`,
4. That requires `._copy_subtree()` to use only methods available to the `TreeNode` class, to iterate over the subtree efficiently,
5. That might require using some methods that are currently only defined on the `NamedNode` class, particularly `.relative_to` (not totally sure about that yet),
6. `.relative_to` requires knowing the node `.name`, which implies perhaps we should merge `TreeNode` and `NamedNode` (which was suggested previously by @shoyer anyway). | closed | 2024-09-11T14:41:53Z | 2024-09-12T19:13:10Z | https://github.com/pydata/xarray/issues/9478 | [
"topic-internals",
"topic-DataTree"
] | TomNicholas | 0 |
koxudaxi/fastapi-code-generator | pydantic | 251 | Illegal FastAPI constructor generated (AttributeError: 'str' object has no attribute 'get': server_data.get("url")) | Using default main template:
```jinja2
app = FastAPI(
{% if info %}
{% for key,value in info.items() %}
{{ key }} = "{{ value }}",
{% endfor %}
{% endif %}
)
```
Generates invalid `FastAPI` constructor when servers are used:
```python
app = FastAPI(
title="App title",
description="App desc",
version="0.1",
servers="[{'url': 'http://api.example.com/v1', 'description': 'App desc'}, {'url': 'http://staging-api.example.com', 'description': 'App desc'}]",
)
```
(note the `servers` key value is actually a string).
Full stacktrace:
```
File "/CODE/repo/openapi/main.py", line 23, in <module>
app = FastAPI(
File "/CODE/.repo-venv/lib/python3.10/site-packages/fastapi/applications.py", line 146, in __init__
self.setup()
File "/CODE/.repo-venv/lib/python3.10/site-packages/fastapi/applications.py", line 216, in setup
server_urls = {url for url in urls if url}
File "/CODE/.repo-venv/lib/python3.10/site-packages/fastapi/applications.py", line 216, in <setcomp>
server_urls = {url for url in urls if url}
File "/CODE/.repo-venv/lib/python3.10/site-packages/fastapi/applications.py", line 215, in <genexpr>
urls = (server_data.get("url") for server_data in self.servers)
AttributeError: 'str' object has no attribute 'get'
```
Removing the `"` from the template in `{{ key }} = "{{ value }}"` does not help as it breaks the scalar keys. What do help is removing `servers` from OpenApi spec.
Version 0.3.5. | closed | 2022-05-17T11:17:20Z | 2023-08-16T11:27:59Z | https://github.com/koxudaxi/fastapi-code-generator/issues/251 | [] | olivergondza | 3 |
graphdeco-inria/gaussian-splatting | computer-vision | 464 | SIBR_gaussianViewer got crash | I got crash when using this viewer. Anybody help pleas. Thank you so much
| closed | 2023-11-12T16:51:36Z | 2023-11-13T17:52:41Z | https://github.com/graphdeco-inria/gaussian-splatting/issues/464 | [] | ryudokid | 4 |
ageitgey/face_recognition | machine-learning | 1,483 | New release? | I think it might be time for a new release. | open | 2023-03-28T17:30:09Z | 2023-03-28T17:30:09Z | https://github.com/ageitgey/face_recognition/issues/1483 | [] | MatthijsBurgh | 0 |
marimo-team/marimo | data-science | 3,688 | marimo.chat: ChatModelConfig maxTokens | ### Documentation is
- [ ] Missing
- [ ] Outdated
- [x] Confusing
- [ ] Not sure?
### Explain in Detail
In https://docs.marimo.io/api/inputs/chat/#supported-model-providers the parameter "max_tokens" of the class ChatModelConfig isn't working with the OpenAI LLMs.
Obviously, there is a misspelling. If I'm using the parameter "maxTokens" then everything works correctly.
### Your Suggestion for Changes
Either the implementation is changed so that "max_tokens" is accepted. I would prefer this option.
Or the documentation is changed. | closed | 2025-02-04T20:39:06Z | 2025-02-05T01:08:18Z | https://github.com/marimo-team/marimo/issues/3688 | [
"documentation"
] | netzrac | 1 |
AUTOMATIC1111/stable-diffusion-webui | pytorch | 16,876 | [Bug]: function parse_generation_parameter removes lastline if multiple loras are embedded in final text | ### Checklist
- [x] The issue exists after disabling all extensions
- [x] The issue exists on a clean installation of webui
- [ ] The issue is caused by an extension, but I believe it is caused by a bug in the webui
- [x] The issue exists in the current version of the webui
- [x] The issue has not been reported before recently
- [ ] The issue has been reported before but has not been fixed yet
### What happened?
File: modules/infotext_utils.py
Line(s): 255 -> if len(re_param.findall(lastline)) < 3:
The function parse_generation_parameters from modules/infotext_utils.py has a built in functionality to ignore the last line of a text passed in if len(re_param.findall(lastline)) < 3, when this criteria is met the last line will not be added to the lines variable list.
### Steps to reproduce the problem
Under the scenario a standalone prompt text such as this below is passed in, the last line will be ignored:
promptDescription1, promptDescription2,
<lora: loraname1 v1:1>, <lora: loraname2 v1:1>, <lora: loraname3 v1:1>, extraDescription, etc
### What should have happened?
I believe the loras format <lora: loraname> should be included in the regex so it does not ignore it when multiple ones are called in the last line.
### What browsers do you use to access the UI ?
Mozilla Firefox
### Sysinfo
Could not generate file
### Console logs
```Shell
no console log errors for this bug
```
### Additional information
More curious as to if this is intended to not detect multiple lora formats for a last line in text since most geninfo are not composed of only the prompt. | open | 2025-03-02T09:50:19Z | 2025-03-06T13:10:36Z | https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/16876 | [
"bug-report"
] | thundaga | 6 |
matplotlib/matplotlib | matplotlib | 29,216 | [Bug]: | ### Bug summary
scatter with small s value leads to hollow point
### Code for reproduction
```Python
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
x = np.random.randn(10)
y = np.random.randn(10)
plt.scatter(x, y, s=0.1)
plt.savefig('scatter.pdf')
```
### Actual outcome
### Expected outcome
the scatter should be not hollow, even for small s value
### Additional information
_No response_
### Operating system
_No response_
### Matplotlib Version
3.9.2
### Matplotlib Backend
_No response_
### Python version
_No response_
### Jupyter version
_No response_
### Installation
None | closed | 2024-12-02T03:44:19Z | 2024-12-03T00:29:34Z | https://github.com/matplotlib/matplotlib/issues/29216 | [
"status: needs clarification"
] | ZhenyuanJin | 5 |
recommenders-team/recommenders | deep-learning | 1,968 | [ASK] No module named 'recommenders' | ### Description
Hi, I try to from recommenders.models.tfidf.tfidf_utils import TfidfRecommender, then get error: No module named 'recommenders', So I use :!pip install git+https://github.com/microsoft/recommenders.git, in google colab get error again : ERROR: Package 'recommenders' requires a different Python: 3.10.12 not in '<3.10,>=3.6', so I change environment in anaconda, get another error: ERROR: Could not build wheels for safetensors, which is required to install pyproject.toml-based projects. Seems can't !pip in google colab and anaconda, is anyone has same problem like me?
### Other Comments
| closed | 2023-08-16T04:40:12Z | 2023-08-17T18:51:55Z | https://github.com/recommenders-team/recommenders/issues/1968 | [
"help wanted",
"duplicate"
] | shasha920 | 2 |
deeppavlov/DeepPavlov | nlp | 1,626 | ModuleNotFoundError: No module named 'torch' | Want to contribute to DeepPavlov? Please read the [contributing guideline](http://docs.deeppavlov.ai/en/master/devguides/contribution_guide.html) first.
Please enter all the information below, otherwise your issue may be closed without a warning.
**DeepPavlov version** deeppavlov-1.0.2
**Python version** 3.9.9:
**Operating system** windows:
**Issue**:
Ошибка
ModuleNotFoundError: No module named 'torch'
Конечно очевидно, что нужно просто установить torch, но для пользователя python уже становится привычным, что зависимости тянутся автоматически при pip install.
| closed | 2023-02-14T04:26:07Z | 2023-03-15T15:43:24Z | https://github.com/deeppavlov/DeepPavlov/issues/1626 | [
"bug"
] | Xtreemrus | 1 |
scrapy/scrapy | web-scraping | 6,630 | build error | <!--
build error
-->
### Description
myenvdeveloper@developerdeMac-mini fanyi % scrapy crawl fy1
2025-01-24 11:26:25 [scrapy.utils.log] INFO: Scrapy 2.12.0 started (bot: fanyi)
2025-01-24 11:26:25 [scrapy.utils.log] INFO: Versions: lxml 5.3.0.0, libxml2 2.12.9, cssselect 1.2.0, parsel 1.10.0, w3lib 2.2.1, Twisted 24.11.0, Python 3.11.6 (main, Jan 24 2025, 10:24:22) [Clang 16.0.0 (clang-1600.0.26.6)], pyOpenSSL 25.0.0 (OpenSSL 3.4.0 22 Oct 2024), cryptography 44.0.0, Platform macOS-15.2-arm64-arm-64bit
2025-01-24 11:26:25 [scrapy.addons] INFO: Enabled addons:
[]
2025-01-24 11:26:25 [scrapy.utils.log] DEBUG: Using reactor: twisted.internet.selectreactor.SelectReactor
2025-01-24 11:26:25 [scrapy.extensions.telnet] INFO: Telnet Password: 1969f05accbbc054
2025-01-24 11:26:25 [scrapy.middleware] INFO: Enabled extensions:
['scrapy.extensions.corestats.CoreStats',
'scrapy.extensions.telnet.TelnetConsole',
'scrapy.extensions.memusage.MemoryUsage',
'scrapy.extensions.logstats.LogStats']
2025-01-24 11:26:25 [scrapy.crawler] INFO: Overridden settings:
{'BOT_NAME': 'fanyi',
'CONCURRENT_REQUESTS': 4,
'CONCURRENT_REQUESTS_PER_DOMAIN': 2,
'DOWNLOAD_DELAY': 3,
'DOWNLOAD_TIMEOUT': 86400,
'DUPEFILTER_CLASS': 'scrapy_splash.SplashAwareDupeFilter',
'HTTPCACHE_STORAGE': 'scrapy_splash.SplashAwareFSCacheStorage',
'NEWSPIDER_MODULE': 'fanyi.spiders',
'SPIDER_MODULES': ['fanyi.spiders']}
2025-01-24 11:26:25 [scrapy.middleware] INFO: Enabled downloader middlewares:
['scrapy.downloadermiddlewares.offsite.OffsiteMiddleware',
'scrapy.downloadermiddlewares.httpauth.HttpAuthMiddleware',
'scrapy.downloadermiddlewares.downloadtimeout.DownloadTimeoutMiddleware',
'scrapy.downloadermiddlewares.defaultheaders.DefaultHeadersMiddleware',
'fanyi.middlewares.MyUserAgentMiddleware',
'scrapy.downloadermiddlewares.useragent.UserAgentMiddleware',
'fanyi.middlewares.FanyiDownloaderMiddleware',
'scrapy.downloadermiddlewares.retry.RetryMiddleware',
'scrapy.downloadermiddlewares.redirect.MetaRefreshMiddleware',
'scrapy.downloadermiddlewares.redirect.RedirectMiddleware',
'scrapy.downloadermiddlewares.cookies.CookiesMiddleware',
'scrapy_splash.SplashCookiesMiddleware',
'scrapy_splash.SplashMiddleware',
'scrapy.downloadermiddlewares.httpproxy.HttpProxyMiddleware',
'scrapy.downloadermiddlewares.httpcompression.HttpCompressionMiddleware',
'scrapy.downloadermiddlewares.stats.DownloaderStats']
2025-01-24 11:26:25 [scrapy.middleware] INFO: Enabled spider middlewares:
['scrapy.spidermiddlewares.httperror.HttpErrorMiddleware',
'scrapy_splash.SplashDeduplicateArgsMiddleware',
'fanyi.middlewares.FanyiSpiderMiddleware',
'scrapy.spidermiddlewares.referer.RefererMiddleware',
'scrapy.spidermiddlewares.urllength.UrlLengthMiddleware',
'scrapy.spidermiddlewares.depth.DepthMiddleware']
2025-01-24 11:26:25 [scrapy.middleware] INFO: Enabled item pipelines:
['fanyi.pipelines.FanyiDownloadFilePipeline', 'fanyi.pipelines.FanyiPipeline']
2025-01-24 11:26:25 [scrapy.core.engine] INFO: Spider opened
Unhandled error in Deferred:
2025-01-24 11:26:25 [twisted] CRITICAL: Unhandled error in Deferred:
Traceback (most recent call last):
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/twisted/internet/defer.py", line 2017, in _inlineCallbacks
result = context.run(gen.send, result)
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/scrapy/crawler.py", line 154, in crawl
yield self.engine.open_spider(self.spider, start_requests)
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/twisted/internet/defer.py", line 2017, in _inlineCallbacks
result = context.run(gen.send, result)
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/scrapy/core/engine.py", line 386, in open_spider
scheduler = build_from_crawler(self.scheduler_cls, self.crawler)
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/scrapy/utils/misc.py", line 187, in build_from_crawler
instance = objcls.from_crawler(crawler, *args, **kwargs) # type: ignore[attr-defined]
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/scrapy/core/scheduler.py", line 208, in from_crawler
dupefilter=build_from_crawler(dupefilter_cls, crawler),
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/scrapy/utils/misc.py", line 187, in build_from_crawler
instance = objcls.from_crawler(crawler, *args, **kwargs) # type: ignore[attr-defined]
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/scrapy/dupefilters.py", line 96, in from_crawler
return cls._from_settings(
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/scrapy/dupefilters.py", line 109, in _from_settings
return cls(job_dir(settings), debug, fingerprinter=fingerprinter)
builtins.TypeError: SplashAwareDupeFilter.__init__() got an unexpected keyword argument 'fingerprinter'
2025-01-24 11:26:25 [twisted] CRITICAL:
Traceback (most recent call last):
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/twisted/internet/defer.py", line 2017, in _inlineCallbacks
result = context.run(gen.send, result)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/scrapy/crawler.py", line 154, in crawl
yield self.engine.open_spider(self.spider, start_requests)
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/twisted/internet/defer.py", line 2017, in _inlineCallbacks
result = context.run(gen.send, result)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/scrapy/core/engine.py", line 386, in open_spider
scheduler = build_from_crawler(self.scheduler_cls, self.crawler)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/scrapy/utils/misc.py", line 187, in build_from_crawler
instance = objcls.from_crawler(crawler, *args, **kwargs) # type: ignore[attr-defined]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/scrapy/core/scheduler.py", line 208, in from_crawler
dupefilter=build_from_crawler(dupefilter_cls, crawler),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/scrapy/utils/misc.py", line 187, in build_from_crawler
instance = objcls.from_crawler(crawler, *args, **kwargs) # type: ignore[attr-defined]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/scrapy/dupefilters.py", line 96, in from_crawler
return cls._from_settings(
^^^^^^^^^^^^^^^^^^^
File "/Users/developer/.pyenv/versions/3.11.6/envs/myenv311/lib/python3.11/site-packages/scrapy/dupefilters.py", line 109, in _from_settings
return cls(job_dir(settings), debug, fingerprinter=fingerprinter)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: SplashAwareDupeFilter.__init__() got an unexpected keyword argument 'fingerprinter'
### Steps to Reproduce
1. pip install scrapy scrapy-splash
2. scrapy crawl fy1
### Versions
myenvdeveloper@developerdeMac-mini fanyi % scrapy version --verbose
Scrapy : 2.12.0
lxml : 5.3.0.0
libxml2 : 2.12.9
cssselect : 1.2.0
parsel : 1.10.0
w3lib : 2.2.1
Twisted : 24.11.0
Python : 3.11.6 (main, Jan 24 2025, 10:24:22) [Clang 16.0.0 (clang-1600.0.26.6)]
pyOpenSSL : 25.0.0 (OpenSSL 3.4.0 22 Oct 2024)
cryptography : 44.0.0
Platform : macOS-15.2-arm64-arm-64bit
| closed | 2025-01-24T03:29:38Z | 2025-01-24T07:35:16Z | https://github.com/scrapy/scrapy/issues/6630 | [] | plkgq | 1 |
bigscience-workshop/petals | nlp | 8 | [DESIGN] auction-like priorities for servers | [for the record: this was proposed by @TimDettmers ]
Currently, hivemind-server treats all requests on a first come first served basis.
If we want to reward active participants with faster inference/training, we could change that into an auction.
Here's how client-server interaction looks like:
- server gives client its stats, the current-highest bid, and maybe some metadata for bidding, e.g. the lowest serviced bids over last T seconds
- client makes a bid - and signs it in such a way that it becomes a commitment ( see #6 )
- [in TaskPool.iterate_minibatches](https://github.com/learning-at-home/hivemind/blob/master/hivemind/moe/server/task_pool.py#L124), server will now generate minibatches in the order of highest bid first
- [in TaskPool.priority](https://github.com/learning-at-home/hivemind/blob/master/hivemind/moe/server/task_pool.py#L175), server will now set pool's priority based on highest bid in the pool, instead of wait time
As suggested by @GreenFatGuy , we need to think through how to deal with situations when low bids on high-demand servers won't ever be processed, and will hence take up memory on both client and server. First order solution: add absolute expiration time to each request, drop requests that hit expiration time. | closed | 2022-06-12T04:12:30Z | 2023-01-02T19:14:32Z | https://github.com/bigscience-workshop/petals/issues/8 | [] | justheuristic | 3 |
donnemartin/system-design-primer | python | 626 | Large scale system | open | 2021-12-05T11:19:43Z | 2022-04-23T13:17:56Z | https://github.com/donnemartin/system-design-primer/issues/626 | [
"needs-review"
] | abdou-c | 1 | |
CorentinJ/Real-Time-Voice-Cloning | deep-learning | 409 | Toolbox: Disable "Load" button when dataset is not loaded | We should also disable the "Load" button in the GUI when a dataset is not loaded. New users click the Load button and are confused when it throws an exception. This has happened several times, most recently in #407 .
Current interface as of #402:
 | closed | 2020-07-09T03:00:59Z | 2020-07-10T07:55:24Z | https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/409 | [] | ghost | 1 |
slackapi/bolt-python | fastapi | 1,105 | Scaling with socket mode | How can I scale the application connected using socket mode?
Based on the documentation, it looks like I can have up to 10 connections open without any way of scaling more horizontally or vertically.
### The page URLs
* https://api.slack.com/apis/socket-mode
## Requirements
Please read the [Contributing guidelines](https://github.com/slackapi/bolt-python/blob/main/.github/contributing.md) and [Code of Conduct](https://slackhq.github.io/code-of-conduct) before creating this issue or pull request. By submitting, you are agreeing to those rules.
| closed | 2024-07-02T10:57:23Z | 2024-07-04T23:18:13Z | https://github.com/slackapi/bolt-python/issues/1105 | [
"duplicate",
"question"
] | Keeo | 2 |
deezer/spleeter | deep-learning | 707 | [Bug] If the -s is greater than 2 hours, the output file is only 1kb and cannot be played. | - [x] I didn't find a similar issue already open.
- [x] I read the documentation (README AND Wiki)
- [x] I have installed FFMpeg
- [ ] My problem is related to Spleeter only, not a derivative product (such as Webapplication, or GUI provided by others)
## Description
<!-- Give us a clear and concise description of the bug you are reporting. -->
## Step to reproduce
<!-- Indicates clearly steps to reproduce the behavior: -->
1. Installed using `pip`
2. Run as `python3.9.7` `SpleeterGui`
3. set -s greater than 2h,SpleeterGui will happen greater than 12h
4. output files cannot play and only 1kb
## Output
```bash
INFO:spleeter:File .\462124074-1-30232/accompaniment.wav written succesfully
INFO:spleeter:File .\462124074-1-30232/vocals.wav written succesfully
```
## Environment
<!-- Fill the following table -->
| | |
| ----------------- | ------------------------------- |
| OS | Windows 10 |
| Installation type | pip |
| RAM available| 16GB |
| Hardware spec | G3 3500china Intel-i7-10870H nvidia RTX-2060 |
## Additional context
I need audio files over 1day Length to train speaker model,but files too big.
| closed | 2022-01-07T22:29:00Z | 2022-01-07T23:45:00Z | https://github.com/deezer/spleeter/issues/707 | [
"bug",
"invalid"
] | EnderOcelot | 0 |
graphql-python/graphene | graphql | 1,404 | Found different types with the same name in the schema | Bear with me please
Let's assume I have a type called `Product` in schema1 and a type with the same name in schema2 and I want to expose __both__ schemas via the same API, I get an error saying that I can't have two types with the same name, even though I'm using TOTALLY different types from different files
```
# file
class Product(...):
pass
# another file
class Product(...):
pass
```
The workaround is to rename the type to something like ProductX or smth, but I want to know why does this specific behavior happen, is there any kind of _global_ registry that I'm not aware of? I've ran through the code but I found nothing | closed | 2022-01-30T02:37:22Z | 2024-06-22T10:22:58Z | https://github.com/graphql-python/graphene/issues/1404 | [
"🐛 bug"
] | LeOndaz | 3 |
Layout-Parser/layout-parser | computer-vision | 172 | How I can extract Titles, Headers , Photos and respective article information from Newspaper? | Hi,
I have been trying to implement the Newspaper navigator model for my application. However, it is able to detect the regions like title or whole article. But I want to extract title and its below paragraphs for my usecase. How I can do that? Please help me to resolve this issue. Is their any tutorial available to guide on it?
Thanks
| open | 2023-03-11T09:30:15Z | 2023-10-04T09:32:18Z | https://github.com/Layout-Parser/layout-parser/issues/172 | [] | karndeepsingh | 1 |
django-cms/django-cms | django | 7,732 | [BUG] Click "New Draft" cause error UNIQUE constraint failed: cms_pagecontent.language, cms_pagecontent.page_id | Click "New Draft" cause error UNIQUE constraint failed: cms_pagecontent.language, cms_pagecontent.page_id
Django Version: | 4.2.9
-- | --
IntegrityError
UNIQUE constraint failed: cms_pagecontent.language, cms_pagecontent.page_id
\.env\Lib\site-packages\django\db\backends\sqlite3\base.py, line 328, in execute djangocms_versioning.admin.edit_redirect_view
\.env\Scripts\python.exe 3.11.7
| closed | 2024-01-03T16:31:56Z | 2025-02-03T06:59:21Z | https://github.com/django-cms/django-cms/issues/7732 | [
"4.1"
] | TLuesebrinck | 15 |
xinntao/Real-ESRGAN | pytorch | 144 | Link to realesrgan-ncnn-vulkan from README.md | I used the link from the README to download : `realesrgan-ncnn-vulkan`.
To my confusion, `realesrgan-x4plus-anime.bin` and `realesrgan-x4plus-anime.param` files are nowhere to be found.
It's because the links from the README is a link to an old version. v0.1.2 | open | 2021-11-02T06:16:33Z | 2021-11-02T09:36:03Z | https://github.com/xinntao/Real-ESRGAN/issues/144 | [] | justUmen | 0 |
autogluon/autogluon | data-science | 4,778 | [BUG] leaderboard don't have score_test column | **Describe the bug**
leaderboard don't have score_test column
this is my score:
```
,model,score_test,score_val,pred_time_test,pred_time_val,fit_time_marginal,fit_order,MAE,MAPE,MASE,MSE,RMSLE,RMSSE,SMAPE,WAPE
0,WeightedEnsemble,,-0.12049423885675616,1509.9086158275604,1127.7490582466125,6.181342363357544,13,,,,,,,,
1,TiDE,,-0.12476309357860434,118.49078869819641,36.36528301239014,611.4046132564545,12,,,,,,,,
2,TemporalFusionTransformer,,-0.13627036644945328,100.78860259056091,23.59303569793701,206.16117525100708,9,,,,,,,,
3,SeasonalNaive,,-0.660805435228755,33.18238949775696,41.93700838088989,15.594502210617065,1,,,,,,,,
4,RecursiveTabular,,-0.15038114804071967,122.86139273643494,7.5854315757751465,563.4693095684052,2,,,,,,,,
5,PatchTST,,-0.1542016456512023,132.4857325553894,16.203494548797607,85.02262091636658,11,,,,,,,,
6,NPTS,,-0.6918072100454226,311.7031497955322,38.06069493293762,15.793499231338501,4,,,,,,,,
7,DynamicOptimizedTheta,,-0.12378074038959759,293.6147825717926,158.2078046798706,16.762980699539185,5,,,,,,,,
8,DirectTabular,,-0.13990292055189926,67.13186550140381,13.225001811981201,374.5207350254059,3,,,,,,,,
9,DeepAR,,-0.15540650115048688,82.5639443397522,26.561711311340332,283.0971758365631,10,,,,,,,,
10,ChronosZeroShot[bolt_base],,-0.12829275030407586,134.53621864318848,11.037777662277222,0.0044155120849609375,7,,,,,,,,
11,ChronosFineTuned[bolt_small],,-0.12416203701720428,83.0649847984314,6.950927972793579,77.18166732788086,8,,,,,,,,
12,AutoETS,,-0.122765501797385,319.86651253700256,808.5811042785645,17.70991277694702,6,,,,,,,,
```
**Expected behavior**
give me score_test
**To Reproduce**
```python
TimeSeriesPredictor.load(f"model").leaderboard(
test_data,
extra_metrics=[
"MAE",
"MAPE",
"MASE",
"MSE",
"RMSLE",
"RMSSE",
"SMAPE",
"WAPE",
],
).to_csv(f"score.csv")
```
**Screenshots / Logs**
nolog
**Installed Versions**
<!-- Please run the following code snippet: -->
<details>
```python
INSTALLED VERSIONS
------------------
date : 2025-01-08
time : 22:15:58.292015
python : 3.12.3.final.0
OS : Linux
OS-release : 5.15.0-60-generic
Version : #66-Ubuntu SMP Fri Jan 20 14:29:49 UTC 2023
machine : x86_64
processor : x86_64
num_cores : 128
cpu_ram_mb : 1031709.55859375
cuda version : 12.535.104.12
num_gpus : 1
gpu_ram_mb : [79943]
avail_disk_size_mb : 1335921416
accelerate : 0.34.2
autogluon : 1.2
autogluon.common : 1.2
autogluon.core : 1.2
autogluon.features : 1.2
autogluon.multimodal : 1.2
autogluon.tabular : 1.2
autogluon.timeseries : 1.2
boto3 : 1.35.82
catboost : 1.2.7
coreforecast : 0.0.12
defusedxml : 0.7.1
einops : 0.8.0
evaluate : 0.4.3
fastai : 2.7.18
fugue : 0.9.1
gluonts : 0.16.0
huggingface-hub : 0.27.0
hyperopt : 0.2.7
imodels : None
jinja2 : 3.1.4
joblib : 1.4.2
jsonschema : 4.21.1
lightgbm : 4.5.0
lightning : 2.4.0
matplotlib : 3.10.0
mlforecast : 0.13.4
networkx : 3.4.2
nlpaug : 1.1.11
nltk : 3.8.1
numpy : 1.26.4
nvidia-ml-py3 : 7.352.0
omegaconf : 2.2.3
onnx : None
onnxruntime : None
onnxruntime-gpu : None
openmim : 0.3.9
optimum : None
optimum-intel : None
orjson : 3.10.12
pandas : 2.2.3
pdf2image : 1.17.0
Pillow : 11.0.0
psutil : 6.1.0
pyarrow : 18.1.0
pytesseract : 0.3.10
pytorch-lightning : 2.4.0
pytorch-metric-learning: 2.3.0
ray : 2.39.0
requests : 2.32.3
scikit-image : 0.24.0
scikit-learn : 1.5.2
scikit-learn-intelex : None
scipy : 1.14.1
seqeval : 1.2.2
skl2onnx : None
spacy : 3.7.5
statsforecast : 1.7.8
tabpfn : None
tensorboard : 2.18.0
text-unidecode : 1.3
timm : 1.0.3
torch : 2.5.1
torchmetrics : 1.2.1
torchvision : 0.20.1
tqdm : 4.67.1
transformers : 4.47.0
utilsforecast : 0.2.4
vowpalwabbit : None
xgboost : 2.1.3
```
</details>
| closed | 2025-01-08T13:19:31Z | 2025-01-10T08:37:29Z | https://github.com/autogluon/autogluon/issues/4778 | [
"bug: unconfirmed",
"Needs Triage",
"module: timeseries"
] | ghost | 6 |
idealo/image-super-resolution | computer-vision | 14 | Inference of large images | I think it would be a great feature to have some option for prediction of larger images, by applying sr to tiles of the main image and stitching the results, this is after enabling gpu inference of course.
Thanks! | open | 2019-04-01T18:11:51Z | 2019-08-29T12:11:34Z | https://github.com/idealo/image-super-resolution/issues/14 | [
"enhancement"
] | ontheway16 | 16 |
Gozargah/Marzban | api | 932 | Json Subscription link does not work on Hiddify apk | برنامه هیدیفای از لینک اشتراک json مرزبان پشتیبانی نمیکنه. نمیدونم مشکل از برنامه هیدیفای هست یا لينک اشتراک، ولی لطفا با بچههای هیدیفای رایزنی و مشکل را حل کنید.
ممنون | closed | 2024-04-09T21:09:34Z | 2024-04-09T21:54:01Z | https://github.com/Gozargah/Marzban/issues/932 | [
"Invalid"
] | farshadl | 1 |
dynaconf/dynaconf | django | 414 | Update the docs version on every release autoamtically | Right now the version shown in the docs page is taken from: https://github.com/rochacbruno/dynaconf/blob/master/mkdocs.yml#L1
https://github.com/rochacbruno/dynaconf/blob/9f86c52ea644862d326079589dfd9c6fd02eecf3/mkdocs.yml#L1
We need to edit the `./release.sh` so it can update that version on the mkdocs.yml the same way it does with VERSION file. | closed | 2020-09-16T15:38:34Z | 2020-09-17T06:06:28Z | https://github.com/dynaconf/dynaconf/issues/414 | [
"help wanted",
"Not a Bug",
"RFC"
] | rochacbruno | 1 |
ageitgey/face_recognition | python | 1,203 | 为什么小孩的识别效果不好？ | 是因为人脸对齐时，小孩的人脸对齐效果不好造成的么？ | open | 2020-08-20T10:02:36Z | 2020-08-20T10:02:36Z | https://github.com/ageitgey/face_recognition/issues/1203 | [] | yfq512 | 0 |
strawberry-graphql/strawberry | fastapi | 3,703 | Support for nullable Connection | The field using `strawberry.connection` should be able to be nullable.
## Feature Request Type
- [ ] Core functionality
- [x] Alteration (enhancement/optimization) of existing feature(s)
- [x] New behavior
## Description
This is needed in case if connection field would return an error e.g. from `PermissionExtension` and we don't want to error out the whole query.
Right now when the field has a declared type of `strawberry.relay.ListConnection[...] | None` the following error is thrown
```
strawberry.relay.exceptions.RelayWrongAnnotationError: Wrong annotation used on field "...". It should be annotated with a "Connection" subclass.
``` | closed | 2024-11-18T14:39:09Z | 2025-03-20T15:56:56Z | https://github.com/strawberry-graphql/strawberry/issues/3703 | [] | marmor157 | 3 |
anselal/antminer-monitor | dash | 73 | Add SNMP | I don't even know if this is possible, but could a custom SNMP table be added to allow monitoring via a third party SNMP server? Essentially using AntminerMonitor as a SNMP proxy for use with PRTG or other monitoring software? | closed | 2018-02-19T21:55:55Z | 2018-07-26T02:33:07Z | https://github.com/anselal/antminer-monitor/issues/73 | [
":x: invalid"
] | dmelvin | 1 |
jacobgil/pytorch-grad-cam | computer-vision | 60 | get_gradients() is empty | When extracting the gradient values in the call method of the GradCam class:
```python
self.extractor.get_gradients()[-1].cpu().data.numpy()
```
the get_cradients() call, returns an empty list, and therefore the [-1] produces an IndexError exception.
The only different thing that I am doing is to use a custom ResNet18 model to work with grayscale images that do not follow the standard (224, 224) size. Has anyone faced this problem before? | closed | 2021-02-26T12:14:33Z | 2021-03-03T15:27:11Z | https://github.com/jacobgil/pytorch-grad-cam/issues/60 | [] | josepdecid | 1 |
ray-project/ray | python | 51,167 | [telemetry] RLlib telemetry prior to `ray.init` is not reported | See this test case: https://github.com/ray-project/ray/pull/51161/files#diff-223a4454f64b8d38669989a2f91f3d9c9910c58d1690ca05541485342c2d52b3R32 | open | 2025-03-07T17:39:16Z | 2025-03-07T17:40:53Z | https://github.com/ray-project/ray/issues/51167 | [
"P1",
"rllib"
] | edoakes | 0 |
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